\indistness and \diversity: Quantifying Mechanisms of Data Augmentation
\indistness and \diversity: Quantifying Mechanisms of Data Augmentation Supplement
Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of either distribution shift or augmentation diversity. Inspired by these, we seek to quantify how data augmentation improves model generalization. To this end, we introduce interpretable and easy-to-compute measures: \indistness and \diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two.
Models that achieve state-of-the-art in image classification often use heavy data augmentation strategies. The best techniques use various transforms applied sequentially and stochastically. Though the effectiveness of such a technique is well-established, the mechanism through which these transformations work is not well-understood.
Since early uses of data augmentation in training neural networks, it has been assumed that they work because they simulate realistic samples from the true data distribution: “[augmentation strategies are] reasonable since the transformed reference data is now extremely close to the original data. In this way, the amount of training data is effectively increased” (Bellegarda et al., 1992). Because of this, augmentations have often been designed with the heuristic of incurring minimal distribution shift from the training data.
This rationale does not explain why unrealistic distortions such as cutout (DeVries and Taylor, 2017), SpecAugment (Park et al., 2019), and mixup (Zhang et al., 2017) significantly improve generalization performance. Furthermore, methods do not always transfer across datasets—Cutout, for example, is useful on CIFAR-10 and not on ImageNet (Lopes et al., 2019). Additionally, many augmentation policies heavily modify images by stochastically applying multiple transforms to a single image. Based on this observation, some have proposed that augmentation strategies are effective because they increase the diversity of images seen by the model.
In this complex landscape, claims about diversity and distributional similarity remain unverified heuristics. Without more precise data augmentation science, finding state-of-the-art strategies requires brute force that can cost thousands of GPU hours (Cubuk et al., 2018; Zhang et al., 2019). This highlights a need to specify and measure the relationship between the original training data and the augmented dataset, as relevant to a given model’s performance.
In this paper, we quantify these heuristics. Seeking to understand the mechanisms of augmentation, we focus on single transforms as a foundation. We present an extensive study of 204 different augmentations on CIFAR-10 and 223 on ImageNet, varying both broad transform families and finer transform parameters. Our contributions are:
We introduce \indistness and \diversity: interpretable, easy-to-compute metrics for parameterizing augmentation performance. \indistness quantifies how much an augmentation shifts the training data distribution from that learned by a model. \diversity quantifies the complexity of the augmented data with respect to the model and learning procedure.
We show that performance is dependent on both metrics. In the \indistness-\diversity plane, the best augmentation strategies jointly optimize the two (see Fig 1).
We connect augmentation to other familiar forms of regularization, such as and learning rate scheduling, observing common features of the dynamics: performance can be improved and training accelerated by turning off regularization at an appropriate time.
We show that performance is only improved when a transform increases the total number of unique training examples. The utility of these new training examples is informed by the augmentationâs \indistness and \diversity.
2 Related Work
Since early uses of data augmentation in training neural networks, there has been an assumption that effective transforms for data augmentation are those that produce images from an “overlapping but different” distribution Bengio et al. (2011); Bellegarda et al. (1992). Indeed, elastic distortions as well as distortions in the scale, position, and orientation of training images have been used on MNIST Ciregan et al. (2012); Sato et al. (2015); Simard et al. (2003); Wan et al. (2013), while horizontal flips, random crops, and random distortions to color channels have been used on CIFAR-10 and ImageNet Krizhevsky et al. (2012); Zagoruyko and Komodakis (2016); Zoph et al. (2017). For object detection and image segmentation, one can also use object-centric cropping Liu et al. (2016) or cut-and-paste new objects Dwibedi et al. (2017); Fang et al. (2019); Ngiam et al. (2019).
In contrast, researchers have also successfully used more generic transformations that are less domain-specific, such as Gaussian noise Ford et al. (2019); Lopes et al. (2019), input dropout Srivastava et al. (2014), erasing random patches of the training samples during training DeVries and Taylor (2017); Park et al. (2019); Zhong et al. (2017), and adversarial noise Szegedy et al. (2013). Mixup Zhang et al. (2017) and Sample Pairing Inoue (2018) are two augmentation methods that use convex combinations of training samples.
It is also possible to further improve generalization by combining individual transformations. For example, reinforcement learning has been used to choose more optimal combinations of data augmentation transformations Ratner et al. (2017); Cubuk et al. (2018). Follow-up research has lowered the computation cost of finding such optimal combinations, by either using population based training Ho et al. (2019), density matching Lim et al. (2019), adversarial policy-design that evolves throughout training Zhang et al. (2019), or a reduced search space Cubuk et al. (2019). Despite producing unrealistic outputs, such combinations of augmentations can be highly effective in different tasks Berthelot et al. (2019); Tan and Le (2019); Tan et al. (2019); Xie et al. (2019a, b).
Across these different examples, the role of distribution shift in training remains unclear. Lim et al. (2019); Hataya et al. (2019) have found data augmentation policies by minimizing the distance between the distributions of augmented data and clean data. Recent work found that after training with augmented data, fine-tuning on clean training data for a few more epochs can be beneficial (He et al., 2019), while Touvron et al. (2019) found it beneficial to fine-tune with a test-set resolution that aligns with the training-set resolution.
The true input-space distribution from which a training dataset is drawn remains elusive. To better understand the effect of distribution shift on model performance, many works attempt to estimate it. Often these techniques require training secondary models, such as those based on variational methods (Goodfellow et al., 2014; Kingma and Welling, 2014; Nowozin et al., 2016; Blei et al., 2017). Others have tried to augment the training set by modelling the data distribution directly Tran et al. (2017). Furthermore, recent work has suggested that even unrealistic distribution modelling can be beneficial (Dai et al., 2017).
These methods try to specify the distribution separately from the model they are trying to optimize. As a result, they are insensitive to any interaction between the model and data distribution. Instead, we are interested in a measure of how much the data shifts along directions that are most relevant to the model’s performance.
We performed extensive experiments with various augmentations on CIFAR-10 and ImageNet. Experiments on CIFAR-10 use the WRN-28-2 model Zagoruyko and Komodakis (2016), trained for 78k steps with cosine learning rate decay. Results given are the mean over 10 initializations and reported errors are the standard error on the mean. Errors are generally too small to show on figures. Details on the error analysis are in Sec. C.
Experiments on ImageNet use the ResNet-50 model He et al. (2016), trained for 112.6k steps with a weight decay rate of 1e-4, and a learning rate of 0.2, which is decayed by 10 at epochs 30, 60, and 80.
Images are pre-processed by dividing each pixel value by 255 and normalizing by the data set statistics. Random crop is also applied on all ImageNet models.
When this pre-processed data is not further augmented, we refer to it as “clean data” and a model trained on it as the “clean baseline”. We follow the same implementation details as Cubuk et al. (2018)
For CIFAR-10, test accuracy on the clean baseline is . The validation accuracy is . On ImageNet, the test accuracy is 76.06%.
Unless specified otherwise, data augmentation is applied following standard practice: each time an image is drawn, the given augmentation is applied with a given probability. We call this mode dynamic augmentation. Due to whatever stochasticity is in the transform itself (such as randomly selecting the location for a crop) or in the policy (such as applying a flip only with 50% probability), the augmented image could be different each time. Thus, most of the tested augmentations increase the number of possible distinct images that can be shown during training.
We also perform select experiments using static training. In static augmentation, the augmentation policy (one or more transforms) is applied once to the entire clean training set. Static augmentation does not change the number of unique images in the dataset.
3.1 \indistness: a simple metric for distribution shift
Thus far, heuristics of distribution shift have motivated design of augmentation policies. Inspired by this focus, we introduce a simple metric to quantify how augmentation shifts data with respect to the decision boundary of the clean baseline model.
We start by noting that a trained model is often sensitive to the distribution of the data it was trained on. That is, model performance varies greatly between new samples from the true data distribution and samples from a shifted distribution. Importantly, the distribution captured by the model is different from the input-space distribution, since training dynamics and the model’s implicit biases affect performance. Because the goal of data augmentation is improving model performance, measuring shifts with respect to this captured distribution is more meaningful than measuring shifts in the input-space distribution.
As such, we define \indistness to be the performance difference between the validation accuracy of a model trained on clean data, and the accuracy of the same model tested on an augmented validation set. Here, the augmentation is applied to the validation dataset in one pass, as a static augmentation. More formally we define:
Let and be training and validation datasets drawn IID from the same clean data distribution, and let be derived from by applying a stochastic augmentation strategy, , once to each image in , . Further let be a model trained on and denote the model’s accuracy when evaluated on dataset . The \indistness, , is given by
With this definition, \indistness of zero represents no shift and a negative number suggests that the augmented data is out-of-distribution for the model.
In Fig. 2 we illustrate \indistness with a two-class classification task on a mixture of two Gaussians. Augmentation in this example comprises shift of the means of the Gaussians of the validation data compared to those used for training. Under this shift, we calculate both \indistness and KL divergence of the shifted data with respect to the original data. \indistness changes only when the shift in the data is with respect to the model’s decision boundary, whereas the KL divergence changes even when data is shifted in the direction that is irrelevant to the classification task. In this way, \indistness captures what is relevant to a model: shifts that impact predictions.
This metric has appeared frequently as a measure of a model’s robustness. It has been used to measure robustness to corruptions of images that do not change their semantic content (Azulay and Weiss, 2018; Dodge and Karam, 2017; Ford et al., 2019; Hendrycks and Dietterich, 2018; Rosenfeld et al., 2018; Yin et al., 2019). Here we, turn this around and use it to quantify the shift of augmented data compared to clean data.
has the following advantages as a metric:
It is easy to measure. It requires only clean training of the model in question.
It is independent of any confounding interaction between the data augmentation and the training process, since augmentation is only used on the validation set and applied statically.
It forms a measure of distance sensitive to properties of both the data distribution and the model.
We gain confidence in this metric by comparing it to other potential model-dependant measures of distribution shift. We consider the mean log likelihood of augmented test images(Grathwohl et al., 2019), and the WatanabeâAkaike information criterion (WAIC) (Watanabe, 2010). These other metrics have high correlation with \indistness. Details can be found in Sec. F.
3.2 Diversity: A measure of augmentation complexity
Inspired by the observation that multi-factor augmentation policies such as FlipLR+Crop+Cutout and RandAugment(Cubuk et al., 2019) greatly improve performance, we propose another axis on which to view augmentation policies, which we dub \diversity. This measure is intended to quantify the intuition that augmentations prevent models from over-fitting by increasing the number of samples in the training set; the importance of this is shown in Sec. 4.3.
Based on the intuition that more diverse data should be more difficult for a model to fit, we propose a model-based measure. The \diversity metric in this paper is the final training loss of a model trained with a given augmentation:
Let be an augmentation and be the augmented training data resulting from applying the augmentation, , stochastically. Further, let be the training loss for a model, , trained on . We define the \diversity, , as
Though determining the training loss requires the same amount of work as determining final test accuracy, here we focus on this metric as a tool for understanding.
As with \indistness, \diversity has the advantage that it can capture model-dependent elements of diversity, i.e. it is informed by the class of priors implicit in choosing a model and optimization scheme as well as by the stopping criterion used in training.
Another potential diversity measure is the entropy of the transformed data, . This is inspired by the intuition that augmentations with more degrees of freedom perform better. For discrete transformations, we consider the conditional entropy of the augmented data.
Here is a clean training image and is an augmented image. This measure has the property that it can be evaluated without any training or reference to model architecture. However, calculating the entropy for continuously-varying transforms is less straightforward.
A third proxy for \diversity is the training time needed for a model to reach a given training accuracy threshold. In Sec. E, we show that these three \diversity metrics correlate well with each other, and that they all serve as complementary measure to \indistness for characterizing augmentation performance.
In the remaining sections we describe how the metrics of \diversity and \indistness can be used to understand augmentation performance.
4.1 Augmentation performance is determined by both \indistness and \diversity
Despite the original inspiration to mimic realistic transformations and minimize distribution shift, many state-of-the-art augmentations yield unrealistic images. This suggests that distribution shift alone does not fully describe or predict augmentation performance.
Figure 3(a) (left) measures \indistness across 204 different augmentations for CIFAR-10 and 223 for ImageNet respectively. We find that for the most important augmentations—those that help performance—\indistness is a poor predictor of accuracy. Furthermore, we find many successful augmentations with low \indistness. For example, Rotate(fixed, 45deg, 50%), Cutout(16), and combinations of FlipLR, Crop(32), and Cutout(16) all have \indistness and test accuracy above clean baseline on CIFAR-10. Augmentation details are in Sec. B.
As \indistness does not fully characterize the performance of an augmentation, we seek another metric. To assess the importance of an augmentation’s complexity, we measure \diversity across the same set of augmentations. We find that \diversity is complementary in explaining how augmentations can increase test performance. As shown in Fig. 3(b) and \subrefhuge-c, \indistness and \diversity together provide a much clearer parameterization of an augmentation policy’s benefit to performance. For a fixed level of \diversity, augmentations with higher \indistness are consistently better. Similarly, for a fixed \indistness, it is generally better to have higher \diversity.
A simple case study is presented in Fig. 4. The probability of the transform Rotate(fixed, 60deg) is varied. The accuracy and \indistness are not monotonically related, with the peak accuracy falling at an intermediate value of \indistness. Similarly, accuracy is correlated with \diversity for low probability transformations, but does not track for higher probabilities. The optimal probability for Rotate(fixed, 60deg) lies at an intermediate value of \indistness and \diversity.
To situate the tested augmentations—mostly single transforms—within the context of the state-of-the-art, we tested three high-performance augmentations from literature: mixup (Zhang et al., 2017), AutoAugment (Cubuk et al., 2018), and RandAugment (Cubuk et al., 2019). These are highlighted with a star marker in Fig. 3(b).
More than either of the metrics alone, \indistness and \diversity together provide a useful parameterization of an augmentation’s performance. We now turn to investigating the utility of this tool for explaining other observed phenomena of data augmentations.
4.2 Turning augmentations off may adjust \indistness, \diversity, and performance
The term “regularizer” is ill-defined in the literature, often referring to any technique used to reduce generalization error without necessarily reducing training error (Goodfellow et al., 2016). With this definition, it is widely acknowledged that commonly-used augmentations act as regularizers (Hernández-García and König, 2018; Zhang et al., 2016; Dao et al., 2019). Though this is a broad definition, we notice another commonality across seemingly different kinds of regularizers: various regularization techniques yield boosts in performance (or at least no degradation) if the regularization is turned off at the right time during training. For instance:
Decaying a large learning rate on an appropriate schedule can be better than maintaining a large learning rate throughout training (Zagoruyko and Komodakis, 2016).
Turning off regularization at the right time in training does not hurt performance (Golatkar et al., 2019).
Relaxing architectural constraints mid-training can boost final performance (d’Ascoli et al., 2019).
Turning augmentations off and fine-tuning on clean data can improve final test accuracy (He et al., 2019).
To further study augmentation as a regularizer, we compare the constant augmentation case (with the same augmentation throughout) to the case where the augmentation is turned off partway through training and training is completed with clean data. For each transform, we test over a range of switch-off points and select the one that yields the best final validation or test accuracy on CIFAR-10 and ImageNet respectively. The Switch-off Lift is the resulting increase in final test accuracy, compared to training with augmented data the entire time.
For some poor-performing augmentations, this gain can actually bring the final test accuracy above the baseline, as shown in Fig. 5(b). We additionally observe (Fig. 5(a)) that this test accuracy improvement can happen quite rapidly for both augmentations and for the other regularizers tested. This suggests an opportunity to accelerate training without hurting performance by appropriately switching off regularization. We call this a “slingshot” effect.
Interestingly, the best time for turning off an augmentation is not always close to the end of training, contrary to what is shown in He et al. (2019). For example, without switching, FlipUD(100%) decreases test accuracy by almost 50% compared to clean baseline. When the augmentation is used for only the first third of training, final test accuracy is above the baseline.
He et al. (2019) hypothesized that the gain from turning augmentation off is due to recovery from the augmentation distribution shift. Indeed, for many detrimental transformations, the test accuracy gained by turning off the augmentation merely recovers the clean baseline performance. However, in Fig. 6, we see that for a given value of \indistness, Switch-off Lift can vary. This is most clear for more useful augmentations, suggesting that the Switch-off Lift is derived from more than simply correction of a distribution shift.
A few of the tested augmentations, such as FlipLR(100%), are fully deterministic. Thus, each time an image is drawn in training, it is augmented the same way. When such an augmentation is turned off partway through training, the model then sees images—the clean ones—that are now new. Indeed, when FlipLR(100%) is switched off at the right time, its final test accuracy exceeds that of FlipLR(50%) without switching. In this way, switching augmentation off may adjust for not only low \indistness but also low \diversity.
4.3 Increased effective training set size is crucial for data augmentation
Most of the augmentations we tested and those used in practice have inherent stochasticity and thus may alter a given training image differently each time the image is drawn. In the typical dynamic training mode, these augmentations increase the number of unique inputs seen across training epochs.
To further study how augmentations act as regularizers, we seek to discriminate this increase in effective dataset size from other effects. We train models with static augmentation, as described in Sec. 3. This altered training set is used without further modification during training so that the number of unique training inputs is the same between the augmented and the clean training settings.
For almost all tested augmentations, using static augmentation yields lower test accuracy than the clean baseline. In the tested cases where static augmentation shows a gain (versions of crop), the difference is less than the standard error on the mean. Additionally, the decrease in performance for static augmentation, compared to the clean baseline, is greater for transforms that have lower \indistness and lower \diversity. Finally, static augmentations always perform worse than their non-deterministic, dynamic counterparts. As shown in Fig. 7, these results point to the following conclusion:
Increased effective training set size is crucial to the performance benefit of data augmentation. An augmentation’s \indistness and \diversity inform how useful the additional training examples are.
In this work, we focused on single transforms in an attempt to understand the essential parts of augmentation in a controlled context. This builds a foundation for further exploration, including using these metrics to quantify and design more complex and powerful combinations of augmentations.
Though earlier work has often explicitly focused on just one of these metrics, the priors used have implicitly ensured reasonable values for both. One way to achieve \diversity is to use combinations of many single augmentations, as exemplified in Cubuk et al. (2018). Because the transforms and hyperparameters in Cubuk et al. (2018) were chosen by optimizing performance on proxy tasks, the optimal policies do include high \indistness and low \indistness transforms. Fast AutoAugment (Lim et al., 2019), CTAugment (Berthelot et al., 2019; Sohn et al., 2020), and differentiable RandAugment (Cubuk et al., 2019) all aim to increase \indistness by what Lim et al. (2019) called “density-matching”. However these methods all use the search space of AutoAugment and inherit its \diversity.
On the other hand, Adversarial AutoAugment Zhang et al. (2019) focused on increasing \diversity by optimizing policies to increase the training loss. While this method did not explicitly aim to increase \indistness, it also used transforms and hyperparameters from the AutoAugment search space. Without such a prior, which includes useful \indistness, the goal of maximizing training loss with no other constraints would lead to data augmentation policies that erase all the information from the images.
Our results motivate casting an even wider net when searching for augmentation strategies. Firstly, our work suggests that explicitly optimizing along axes of both \indistness and \diversity yields better performance. Furthermore, we have seen that poor-performing augmentations can actually be helpful if turned off during training (Figure 5). With inclusion of scheduling in augmentation optimization, we expect there are opportunities for including a different set of augmentations in an ideal policy. Ho et al. (2019) observes trends in how probability and magnitude of various transforms change during training for an optimized augmentation schedule. We suggest that with further study, \diversity and \indistness can provide priors for optimization of augmentation schedules.
We attempted to quantify common intuition that more in-distribution and more diverse augmentation policies perform well. To this end, we introduced two easy-to-compute metrics, \indistness and \diversity, intended to measure to what extent a given augmentation is in-distribution and how complex the augmentation is to learn. Because they are model-dependent, these metrics capture the data shifts that affect model performance.
Using these tools, we have conducted a study over a large class of augmentations for CIFAR-10 and ImageNet and found that neither feature alone is a perfect predictor of performance. Rather, we presented evidence that an augmentation’s \diversity and \indistness play dual roles in determining augmentation quality. Optimizing for either metric separately is sub-optimal and the best augmentations balance the two.
Additionally, we found that increased sample number was a necessary ingredient of beneficial augmentation.
Finally, we found that augmentations share an important feature with other regularizers: switching off regularization at the right time can improve performance. In some cases, this can cause an otherwise poorly-performing augmentation to be beneficial.
We hope our findings provide a foundation and context for continued scientific study of data augmentation.
The authors would like to thank Alex Alemi, Justin Gilmer, Guy Gur-Ari, Albin Jones, Behnam Neyshabur, and Ben Poole for thoughtful discussions on this work.
Appendix A Training methods
Cifar10 models are trained using code that is based on AutoAugment code
Learning rate is decayed following a cosine decay schedule, starting with a value of 0.1
78050 training steps, with data shuffled after every epoch.
As implemented in the AutoAugment code, model is WRN-28-2 with stochastic gradient descent and momentum. Optimizer uses cross entropy loss with weight decay of 0.0005.
Before selecting the validation set, the full training set is shuffled and balanced such that the subset selected for training is balanced across classes.
Validation set is the last 5000 samples of the shuffled CIFAR-10 training data.
Models trained using Python 2.7 and TensorFlow 1.13 .
A training time of 78k steps was chosen because it shows reasonable convergence with the standard data augmentation of FlipLR, Crop, and Cutout In the clean baseline case, test accuracy actually reaches its peak much earlier than 78k steps.
With CIFAR-10, experiments were also performed for dataset sizes of 1024, 4096, and 16384. At smaller dataset sizes, the impact of augmentation and the Switch-off Lift tends to be larger. These results are not shown in this paper.
ImageNet models are ResNet-50 trained using the Cloud TPU codebase
Appendix B Details of augmentation
On CIFAR-10, both color and affine transforms were tested, as given in the full results (see Sec. G). Most augmentations are as defined in Cubuk et al. (2018). For Rotate, fixed means each augmented image is rotated by exactly the stated amount, with randomly-chosen direction. Variable means an augmented image is rotated a random amount up to the given value. Shear is defined similarly. Rotate(square) means that an image is rotated by an amount chosen randomly from [0, 90, 180, 270].
Crop includes a padding before the random-location crop so that the final image remains in size. The magnitude given for Crop is the number of pixels that are added in each dimension. The magnitude given in Cutout is the size, in pixels, of each dimension of the square cutout.
PatchGaussian is as defined in Lopes et al. (2019), with the patch specified to be contained entirely within the image domain. It is specified by two hyperparameters: the size of the square patch (in pixels) and , which is the maximum standard deviation of the noise that can be selected for any given patch. Here, “fixed” means the patch size is always the same.
Since FlipLR, Crop, and Cutout are part of standard pipelines for CIFAR-10, we test combinations of the three augmentations (varying probabilities of each) as well as these three augmentations plus an single additional augmentation. As in standard processing of CIFAR-10 images, the first augmentation applied is anything that is not one of FlipLR, Crop, or Cutout. After that, augmentations are applied in the order Crop, then FlipLR, then Cutout.
Finally, we tested the CIFAR-10 AutoAugment policy (Cubuk et al., 2018), RandAugment (Cubuk et al., 2019), and mixup (Zhang et al., 2017). The hyperparameters for these augmentations followed the guidelines described in the respective papers.
These augmentations are labeled in Fig. 8.
On ImageNet, we experimented with PatchGaussian, Cutout, operations from the PIL imaging library
These augmentations are labeled in Fig. 9.
Each augmentation is applied with a certain probability (given as a percentage in the label). Each time an image is pulled for training, the given image is augmented with that probability.
Appendix C Error analysis
All of the CIFAR-10 experiments were repeated with 10 different initialization. In most cases, the standard error on the mean (SEM) is too small to show as error bars on plots. The error on each measurement is given in the full results (see Sec. G).
and Switch-off Lift both are computed from differences between runs that share the same initialization. For \indistness, the same trained model is used for inference on clean validation data and on augmented validation data. Thus, the variance of \indistness for the clean baseline is not independent of the variance of \indistness for a given augmentation. The difference between the augmentation case and the clean baseline case is taken on a per-experiment basis (for each initialization of the clean baseline model) before the error is computed.
In the switching experiments, the final training without augmentation is computed starting from a given checkpoint in the model that is trained with augmentation. Thus, each switching experiment shares an initialization with an experiment that has no switching. Again, in this case the difference is taken on a per-experiment basis before the error (based on the standard deviation) is computed.
All ImageNet experiments shown are with one initialization. Thus, there are not statistics from which to analyze the error.
Appendix D Switching off augmentations
For CIFAR-10, switching times are tested in increments of approximately 5k steps between k and k steps. The best point for switching is determined by the final validation accuracy.
On ImageNet, we tested turning augmentation off at 50, 60, 70, and 80 epochs. Total training takes 90 epochs. The best point for switching is determined by the final test accuracy.
The Switch-off Lift is derived from the experiment at the best switch-off point for each augmentation.
There are some augmentations where the validation accuracy is best at 25k, which means that further testing is needed to find if the actual optimum switch off point is lower or if the best case is to not train at all with the given augmentation. Some of the best augmentations have a small negative Switch-off Lift, indicating that it is better to train the entire time with the given augmentations.
For each augmentation, the best time for switch-off is listed in the full results (see Sec. G).
Appendix E Diversity metrics
We compute three possible diversity metrics, shown in Fig. 10: Entropy, Final Training Loss, Training Steps to Accuracy Threshold. The entropy is calculated only for augmentations that have a discrete stochasticity (such as Rotate(fixed) and not for augmentations that have a continuous variation (such as Rotate(variable) or PatchGaussian). Final Training Loss is the batch statistic at the last step of training. For CIFAR-10 experiments, this is averaged across the 10 initializations. For ImageNet, it is averaged over the last 10 steps of training. Training Steps to Accuracy Threshold is the number of training steps at which the training accuracy first hits a threshold of 97%. A few of the tested augmentation (extreme versions of PatchGaussian) do not reach this threshold in the given time and that column is left blank in the full results.
Entropy is unique in that it is independent of the model or data set and it is a counting of states. However, it is difficult to compare between discrete and continuously-varying transforms and it is not clear how proper it is to compare even across different types of transforms.
Final Training Loss and Training Steps to Accuracy Threshold correlate well across the tested transforms. Entropy is highly correlated to these measures for PatchGaussian and versions of FlipLR, Crop, and Cutout where only probabilities are varying. For Rotate and Shear where magnitudes are varying as well, the correlation between Entropy and the other two measures is less clear.
Appendix F Comparing \indistness to other related measures
We gain confidence in the \indistness measure by comparing it to other potential model-dependant measures of distribution shift. In Fig 11, we show the correlation between \indistness and these two measures: the mean log likelihood of augmented test imagesGrathwohl et al. (2019) (labeled as “logsumexp(logits)”) and the WatanabeâAkaike information criterion (labeled as “WAIC”) Watanabe (2010).
Like \indistness, these other two measures indicate how well a model trained on clean data comprehends augmented data.
Appendix G Full results
The plotted data for CIFAR-10 and ImageNet are given in .csv files uploaded at https://storage.googleapis.com/public_research_data/augmentation/data.zip. In these .csv files, blank cells generally indicate that a given experiment (such as switching) was not done for the specified augmentation. In the case of the training accuracy threshold as a proxy for diversity, a blank cell indicates that for the given augmentation, the training accuracy did not reach the specified threshold during training.
- Available at bit.ly/2v2FojN
- available at github.com/tensorflow/models/tree/master/research/autoaugment
- available at https://github.com/tensorflow/tpu/tree/master/models/official/resnet
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