Learning Robust Global Representations
by Penalizing Local Predictive Power
Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale.
Consider the task of determining whether a photograph depicts a tortoise or a sea turtle. A human might check to see whether the shell is dome-shaped (indicating tortoise) or flat (indicating turtle). She might also check to see whether the feet are short and bent (indicating tortoise) or fin-like and webbed (indicating turtle). However, the pixels corresponding to the turtle (or tortoise) itself are not alone in offering predictive value. As easily confirmed through a Google Image search, sea turtles tend to be photographed in the sea while tortoises tend to be photographed on land.
Although an image’s background may indeed be predictive of the category of the depicted object, it nevertheless seems unsettling that our classifiers should depend so precariously on a signal that is in some sense irrelevant. After all, a tortoise appearing in the sea is still a tortoise and a turtle on land is still a turtle. One reason why we might seek to avoid such a reliance on correlated but semantically unrelated artifacts is that they might be liable to change out-of-sample. Even if all cats in a training set appear indoors, we might require a classifier capable of recognizing an outdoors cat at test time. Indeed, recent papers have attested to the tendency of neural networks to rely on surface statistical regularities rather than learning global concepts (Jo and Bengio, 2017; Geirhos et al., 2019). A number of papers have demonstrated unsettling drops in performance when convolutional neural networks are applied to out-of-domain testing data, even in the absence of adversarial manipulation.
The problem of developing robust classifiers capable of performing well on out-of-domain data is broadly known as Domain Adaptation (DA). While the problem is known to be impossible absent any restrictions on the relationship between training and test distributions (Ben-David et al., 2010b), progress is often possible under reasonable assumptions. Theoretically-principled algorithms have been proposed under a variety of assumptions, including covariate shift (Shimodaira, 2000; Gretton et al., 2009) and label shift (Storkey, 2009; Schölkopf et al., 2012; Zhang et al., 2013; Lipton et al., 2018). Despite some known impossibility results for general DA problems (Ben-David et al., 2010b), in practice, humans exhibit remarkable robustness to a wide variety of distribution shifts, exploiting a variety of invariances, and knowledge about what a label actually means.
Our work is motivated by the intuition that for the classes typically of interest in many image classification tasks, the larger-scale structure of the image is what makes the class apply and while small local patches might be predictive of the label, Such local features, considered independently, should not (vis-a-vis robustness desiderata) comprise the basis for outputting a given classification. Instead, we posit that classifiers that are required to (in some sense) discard this local signal (i.e., patches of an images correlated to the label within a data collection), basing predictions instead on global concepts (i.e., concepts that can only be derived by combining information intelligently across regions), may better mimic the robustness that humans demonstrate in visual recognition.
In this paper, in order to coerce a convolutional neural network to focus on the global concept of an object, we introduce Patch-wise Adversarial Regularization (PAR), a learning scheme that penalizes the predictive power of local representations in earlier layers. The method consists of a patch-wise classifier applied at each spatial location in low-level representation. Via the reverse gradient technique popularized by Ganin et al. (2016), our network is optimized to fool the side classifiers whilst simultaneously optimizing to output correct predictions at the final layer. Design choices of PAR include the layer on which the penalty is applied, the regularization strength, and the number of layers in the patch-wise network-in-network classifier.
In extensive experiments across a wide spectrum of synthetic and real data sets, our method outperforms the competing ones, especially when the domain information is not available. We also take measures to evaluate our model’s ability to learn concepts at real-world scale despite the small scale of popular domain adaptation benchmarks. Thus we introduce a new benchmark dataset that resembles ImageNet in the choice of categories and size, but consists only of images with the aesthetic of hand-drawn sketches. Performances on this new benchmark also endorse our regularization.
2 Related Work
A broad set of papers have addressed various formulations of DA (Bridle and Cox, 1991; Ben-David et al., 2010a) dating in the ML and statistics literature to early works on covariate shift Shimodaira (2000) with antecedents classic econometrics work on sample selection bias (Heckman, 1977; Manski and Lerman, 1977). Several modern works address principled learning techniques under covariate shift (when does not change) (Gretton et al., 2009) and under label shift (when doesn’t change) (Storkey, 2009; Zhang et al., 2013; Lipton et al., 2018), and various other assumptions (e.g. bounded divergences between source and target distributions) (Mansour et al., 2009; Hu et al., 2016).
With the recent success of deep learning methods, a number of heuristic domain adaptation methods have been proposed that despite lacking theoretical backing nevertheless confer improvements on a number of benchmarks, even when traditional assumptions break down (e.g., no shared support). At a high level these methods comprise two subtypes: fine-tuning over target domain (Long et al., 2016; Hoffman et al., 2017; Motiian et al., 2017a; Gebru et al., 2017; Volpi et al., 2018) and coercing domain invariance via adversarial learning (or further extensions) (Ganin et al., 2016; Bousmalis et al., 2017; Tzeng et al., 2017; Xie et al., 2018; Hoffman et al., 2018; Long et al., 2018; Zhao et al., 2018b; Kumar et al., 2018; Li et al., 2018b; Zhao et al., 2018a; Schoenauer-Sebag et al., 2019). While some methods have justified domain-adversarial learning by appealing to theoretical bounds due to Ben-David et al. (2010a), the theory does not in fact guarantee generalization (recently shown by (Johansson et al., 2019; Wu et al., 2019)) and sometimes guarantees failure. For a general primer, we refer the reader to the following reviews (Weiss et al., 2016; Csurka, 2017; Wang and Deng, 2018).
In contrast to the typical unsupervised DA setup, which requires access to both labeled source data and unlabeled target data, several recent papers propose deep learning methods that confer robustness to a variety of distribution shifts (in practice) wihtout requiring any data (even unlabeled data) from the target distribution. In domain generalization (DG) methods (Muandet et al., 2013) (or sometimes known as “zero shot domain adaptation” (Kumagai and Iwata, 2018; Niu et al., 2015; Erfani et al., 2016; Li et al., 2017c)) one possesses domain identifiers for a number of known in-sample domains, and the goal is to generalize to a new domain. More recent DG approaches incorporate adversarial (or similar) techniques (Ghifary et al., 2015; Wang et al., 2016; Motiian et al., 2017b; Li et al., 2018a; Carlucci et al., 2018), or build ensembles of per-domain models that are then fused representations together (Bousmalis et al., 2016; Ding and Fu, 2018; Mancini et al., 2018). Meta-learning techniques have also been explored (Li et al., 2017b; Balaji et al., 2018).
More recently, Wang et al. (2019) demonstrated promising results without requiring domain identifiers. Their method achieves resilience to distribution shifts by incorporating a new component intended to be especially sensitive to domain-specific signals. Our paper extends the set-up of (Wang et al., 2019) and studies the problem of DA without any domain information.
We use to denote the samples and to denote a convolutional neural network, where denotes the output of the bottom convolutional layers (e.g., the first layer), and and are parameters to be learned. Traditional training process aims to solve the following problem
where denotes the loss function, such as cross-entropy loss.
Following the standard set-up of a convolutional layer, is a tensor of parameters, where denotes the number of convolutional channels, and is the size of the convolutional kernel. Therefore, for the th sample, is a representation of of the dimension , where (or ) is a function of the image dimension and (or ). 111The exact function depends on padding size and stride size, and is irrelevant to the discussion of this paper.
3.1 Patch-wise Adversarial Regularization
We first introduce a new classifier, that takes the input of a -length vector and predicts the label. Thus, can be applied onto the representation and yield predictions. Therefore, each of the predictions can be seen as a prediction made only by considering a small image patch corresponding to each of the receptive fields in . If any of the image patches are predictive and summarizes the predictive representation well, can be trained to achieve a high prediction accuracy.
On the other hand, if summarizes the patch-wise predictive representation well, higher layers () can directly utilize these representation for prediction and thus may not be required to learn a global concept. Our intuition is that by regularizing such that each fiber (i.e., representation at the same location from every channel) in the activation tensor should not be individually predictive of the label, we can prevent our model from relying on local patterns and instead force it to learn a pattern that can only be revealed by aggregating information across multiple receptive fields.
As a result, in addition to the standard cost function 1, we also optimize the following term:
where the minimization consists of training to predict the label based on the local features (at each spatial location) while the maximization consists of training to shift focus away from local predictive representations.
We hypothesize that by optimizing Functions 1 and 2 together, we can train a model that can predict the label well, without relying too strongly on simple local patterns. The optimization can be reformulated into the following two functions:
where is a tuning hyperparameter. We divide the loss by to keep the two terms at a same scale.
Our method can be implemented efficiently as follows. In practice, we consider as a fully-connected layer. consists of a weight matrix and a -length bias vector, where is the number of classes. The forward operations as fully-connected networks can be efficiently implemented as one convolutional operation with input channels and output channels operating on the representation.
Note that although the input has vectors, only has one set of parameters that is used for all these vectors, in contrast to building a set of parameter for every receptive field of the dimension. Using only one set of parameter can not only help to reduce the computational load and parameter space, but also help to identify the predictive local patterns well because the predictive local pattern does not necessarily appear at the same position across the images. Our idea of our method is illustrated in Figure 1.
3.2 Other Extensions and Training Heuristics
There can be many simple extensions to the basic PAR setting we discussed above. Here we introduce three extensions that we will experiment with later in the experiment section.
More Powerful Pattern Classifier: We explore the space of discriminator architectures, replacing the single-layer network with a more powerful network architecture, e.g. a multilayer perceptron (MLP). In this paper, we consider three-layer MLPs with ReLU activation functions. We name this variant as PARM.
Broader Local Pattern: We can also extend the convolution operation to enlarge the concept of “local”. In this paper, we experiment with a convolution operation, thus the number of parameters in is increased. We refer to this variant as PARB.
Higher Level of Local Concept: Further, we can also build the regularization upon higher convolutional layers. Building the regularization on higher layers is related to enlarging the patch of image, but also considering higher level of abstractions. In this paper, we experiment the regularization on the second layer. We refer this method as PARH.
Training Heuristics: Finally, we introduce the training heuristic that plays an important role in our regularization technique, especially in modern architectures such as AlexNet or ResNet. The training heuristic is simple: we first train the model conventionally until convergence (or after a certain number of epochs), then train the model with our regularization. In other words, we can also directly work on pretrained models and continue to fine-tune the parameters with our regularization.
In this section, we test PAR over a variety of settings, we first test with perturbed MNIST under domain generalization setting, and then test with perturbed CIFAR10 under domain adaptation setting. Further, we test on more challenging data sets, with PACS data under domain generalization setting and our newly proposed ImageNet-Sketch data set. We compare with previous state-of-the-art when available, or with the most popular benchmarks such as DANN (Ganin et al., 2016), InfoDrop (Achille and Soatto, 2018), and HEX (Wang et al., 2019) on synthetic experiments.
4.1 MNIST with Perturbation
We follow the set-up of Wang et al. (2019) in experimenting with MNIST data set with different superficial patterns. There are three different superficial patterns (radial kernel, random kernel, and original image). The training/validation samples are attached with two of these patterns, while the testing samples are attached with the remaining one. As in Wang et al. (2019), training/validation samples are attached with patterns following two strategies: 1) independently: the pattern is independent of the digit, and 2) dependently: images of digit 0-4 have one pattern while images of digit 5-9 have the other pattern.
We use the same model architecture and learning rate as in Wang et al. (2019). The extra hyperparameter is set as 1 as the most straightforward choice. Methods in Wang et al. (2019) are trained for 100 epochs, so we train the model for 50 epochs as pretraining and 50 epochs with our regularization. The results are shown in Figure 2. In addition to the direct message that our proposed method outperforms competing ones in most cases, it is worth mentioning that the proposed methods behave differently in the “dependent” settings. For example, PARM performs the best in the “original” and “radial” settings, but almost the worst among proposed methods in the “random” setting, which may indicate that the pattern attached by “random” kernel can be more easily detected and removed by PARM during training (Notice that the name of the setting (“original”, “radial” or “random”) indicates the pattern attached to testing images, and the training samples are attached with the other two patterns). More information about hyperparameter choice is in Appendix A.
4.2 CIFAR with Perturbation
We continue to experiment on CIFAR10 data set by modifying the color and texture of test dataset with four different schemas: 1) greyscale; 2) negative color; 3) random kernel; 4) radial kernel. Some examples of the perturbed data are shown in Appendix B. In this experiment, we use ResNet as our base classifier, which has a rough 92% prediction accuracy on original CIFAR10 test data set.
As for PAR, we first train the base classifier for 250 epochs and then train with the adversarial loss for another 150 epochs. As for the competing models, we also train for 400 epochs with carefully selected hyperparameters. The overall performances are shown in Table 1. In general, PAR and its variants achieve the best performances on all four test data sets, even when DANN has an unfair advantage over others by seeing unlabelled testing data during training. To be specific, PAR achieves the best performances on the greyscale and radial kernel settings; PARM is the best on the negative color and random kernel settings. One may argue that the numeric improvements are not significant and PAR may only affect the model marginally, but a closer look at the training process of the methods indicates that our regularization of local patterns benefits the robustness significantly while has little impact on the original performance. More detailed discussions are in Appendix B.
We test on the PACS data set (Li et al., 2017a), which consists of collections of images over four domains, including photo, art painting, cartoon, and sketch. Many recent methods have been tested on this data set, which offers a convenient way for PAR to be compared with the previous state-of-the-art. Following (Li et al., 2017a), we use AlexNet as baseline and build PAR upon it. We compare with recently reported state-of-the-art on this data set, including DSN (Bousmalis et al., 2016), LCNN (Li et al., 2017a), MLDG (Li et al., 2017b), Fusion (Mancini et al., 2018), MetaReg (Balaji et al., 2018) and HEX (Wang et al., 2019), in addition to the baseline reported in (Li et al., 2017a).
|Art||Cartoon||Photo||Sketch||Average||Forgoing Domain ID|
Following the training heuristics we introduced, we continue with trained AlexNet weights222https://www.cs.toronto.edu/~guerzhoy/tf_alexnet/ and fine-tune on training domain data of PACS for 100 epochs. We notice that once our regularization is plugged in, we can outperform the baseline AlexNet with a 2% improvement. We further adopt the normalization technique (i.e., normalizing the representations at every batch) used in (Wang et al., 2019) to improve upon the state-of-the-art. The results are reported in Table 2, where we separate the results of techniques relying on domain identifications and techniques free of domain identifications.
While our methods are in general competitive, it is worth mentioning that our methods improve upon previous methods with a relatively large margin when Sketch is the testing domain. The improvement on Sketch is exciting because Sketch is the only colorless domain out of the four domains in PACS. Therefore, when tested with the other three domains, a model may learn to exploit the color information, which is usually local, to predict, but when tested with Sketch domain, the model has to learn colorless concepts to make good predictions.
4.4.1 The ImageNet-Sketch Data
Inspired by the Sketch data of (Li et al., 2017a) with seven classes, and several other Sketch data sets, such as the Sketchy data set (Sangkloy et al., 2016) with 125 classes and the Quick Draw! data set (QuickDraw, 2018) with 345 classes, as well as motivated by the need of a large-scale sketch data set that is in accordance with popular standard training data sets for testing model’s ability in capturing global semantics, we construct the ImageNet-Sketch data set for evaluating the out-of-domain classification performance of vision models trained on ImageNet.
Compatible with standard ImageNet validation data set for the classification task (Deng et al., 2009), our ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries “sketch of ”, where is the standard class name. We only search within the “black and white” color scheme. We initially query 100 images for every class, and then manually clean the pulled images by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then we augment the data set by flipping and rotating the images.
We expect ImageNet-Sketch to serve as a unique ImageNet-scale out-of-domain evaluation dataset for image classification. Also, notably, different from perturbed ImageNet validation sets (Geirhos et al., 2019; Hendrycks and Dietterich, 2019), the images of ImageNet-Sketch are collected independently from the original validation images. The independent collection procedure is more similar to (Recht et al., 2019), who collected a new set of standard colorful ImageNet validation images. In comparison, our advantage is that the black-and-white sketch images can better serve the goal of testing the model’s ability in abstracting high-level concepts. Sample images are shown in Figure 3.
4.4.2 Experiment Results
We use AlexNet as the baseline and test whether our method can help improve the out-of-domain prediction. We start with ImageNet pretrained AlexNet and continue to use PAR to tune the AlexNet for another five epochs on the original ImageNet training data set. The results are reported in Table 3.
We are particularly interested in how PAR improves upon AlexNet, so we further investigate the top-1 prediction results. Although the numeric results in Table 3 seem to show that PAR only improves the upon AlexNet by predicting a few more examples correctly, we notice that these models share 5025 correct predictions, while AlexNet predicts another 1098 images correctly and PAR predicts a different set of 1617 images correctly.
We first investigate the examples that are correctly predicted by the original AlexNet, but wrongly predicted by PAR. We notice some examples that help verify the performance of PAR. For examples, PAR incorrectly predict three samples of “keyboard” to “crossword puzzle,” while AlexNet predicts these samples correctly. It is exciting to see that two of these samples are “keyboards with missing keys” and hence look similar to a “crossword puzzle.”
|Afghan hound||0.8945||swab (mop)||0.7379|
We also investigate the examples that are correctly predicted by PAR, but wrongly predicted by the original AlexNet. Interestingly, we notice several samples that are wrongly predicted by AlexNet because the model may only focus on the local patterns. Some of the most interesting examples are reported in Table 4: The first example is a stethoscope, PAR predicts it correctly with 0.66 confidence, while AlexNet predicts it to be a hook. We conjecture the reason to be that AlexNet tends to only focus on the curvature which resembles a hook. The second example tells a similar story, PAR predicts tricycle correctly with 0.92 confidence, but AlexNet predicts it as a safety pin with 0.51 confidence. We believe this is because part of the image (likely the seat-supporting frame) resembles the structure of a safety pin. For the third example, PAR correctly predicts it to be an Afghan hound with 0.89 confidence, but AlexNet predicts it as a mop with 0.73 confidence. This is likely because the fur of the hound is similar to the head of a mop. For the last example, PAR correctly predicts the object to be red wine with 0.59 confidence, but AlexNet predicts it to be a goblet with 0.74 confidence. This is likely because part of the image is indeed part of a goblet, but PAR may learn to make predictions based on the global concept considering the bottle, the liquid, and part of the goblet together. Table 4 only highlights a few examples, and more examples are shown in Appendix C.
While we have demonstrated the strong performance of our method on out-of-domain data, we need to clarify that our method does not necessarily help for in-domain accuracy, especially in the cases when local patterns of background truly are predictive for the depicted object. For the three variations we introduced, our experiments indicate that different variants are applicable to different scenarios. We recommend users to decide which variant to use given their understanding of the problem and hope in future work, to develop clear principles for guiding these choices. Concerning the ImageNet-Sketch data set, a potential limitation may exist due to data augmentation. However, there are only a limited number of images whose semantics may be altered by rotations or flips. One probably only needs to be concerned with these limitations when a new method only predicts a couple more images correctly from the old method evaluated by this benchmark.
In this paper, we introduced patch-wise adversarial regularization, a technique that regularizes models, encouraging them to learn global concepts for classifying objects by penalizing the model’s ability to make predictions based on representations of local patches. We extended our basic set-up with several different variants and conducted extensive experiments, evaluating these methods with several datasets for domain adaptation and domain generalization tasks. The experimental results favored our methods, especially when domain information is unknown to the methods. In addition to the superior performances we achieved through these experiments, we expected to further challenge our method at real-world scale. Therefore, we also constructed a dataset that matches the ImageNet classification validation set in classes and scales but contains only sketch-alike images. Our new ImageNet-Sketch data set can serve as new territory for evaluating models’ ability to generalize to out-of-domain images at an unprecedented scale.
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Appendix A Other Hyperparameter Choices for MNIST experiment
We also experimented with the parameter choices of the method in the MNIST experiment. We varied the in in PAR and reported the performance to guide further usage of the method.
As we can see from Figure 4, PAR seems to prefer the cases when is relatively smaller, although what we reported in the main manuscript for the MNIST experiment is as the most straightforward choice, to demonstrate the method’s strength.
Later in other experiments, especially the ImageNet-Sketch experiment, we notice that is too strong (unless the learning rate is set to be much smaller) for the method to work. We observe that a too-strong usually immediate deteriorates the performance during first epoches of training. Therefore, in practice, we recommend the users to set the (or learning rate) to be smaller if the users observe that our method deteriorates the training performance.
Appendix B Cifar10 discussion
Appendix C More results of ImageNet-Sketch
We conducted a detailed analysis of ImageNet-Sketch results with the following rules:
The samples are correctly predicted by one model, but wrongly predicted by the other.
When a model makes wrong predictions, the samples in the class tend to be predicted into a same another class. Therefore, we can exclude some random prediction errors.
For class A and B, if one model tends to predict samples in class A into class B, and the other model has the reverse tendency, we investigate neither of these classes, because the different prediction results may only be due to the similarity of these two classes.
Table 5 shows more samples that are correctly predicted by PAR but wrongly predicted by the original AlexNet, because (as we conjecture) the original AlexNet focuses on local patterns.
|Afghan hound||0.89||swab (mop)||0.74||sunglass||0.42||strainer||0.27|
|Afghan hound||0.92||swab (mop)||0.82||sunglass||0.31||strainer||0.19|
|Afghan hound||0.80||swab (mop)||0.20||sunglass||0.38||strainer||0.32|
|bull mastiff||0.42||shower cap||0.23||totem pole||0.30||envelope||0.39|
|bull mastiff||0.33||shower cap||0.37||totem pole||0.43||envelope||0.27|
|bull mastiff||0.57||shower cap||0.77||totem pole||0.50||envelope||0.40|
|ashcan||0.17||safety pin||0.41||totem pole||0.45||envelope||0.39|
|ashcan||0.38||safety pin||0.26||tricycle||0.17||safety pin||0.42|
|ashcan||0.16||safety pin||0.53||tricycle||0.66||safety pin||0.49|
|car mirror||0.42||buckle||0.89||tricycle||0.07||safety pin||0.12|
|car mirror||0.57||buckle||0.43||tricycle||0.93||safety pin||0.51|
|car mirror||0.88||buckle||0.63||whiskey jug||0.20||perfume||0.14|
|stethoscope||0.77||hook||0.58||head cabbage||0.44||shower cap||0.31|
|stethoscope||0.75||hook||0.42||head cabbage||0.78||shower cap||0.79|
|stethoscope||0.46||hook||0.59||head cabbage||0.34||shower cap||0.39|