Searching for an (un)stable equilibrium: experiments in training generative models without data

Searching for an (un)stable equilibrium: experiments in training generative models without data

Terence Broad
Department of Computing
Goldsmiths, University of London
t.broad@gold.ac.uk
Mick Grierson
Creative Computing Institute
University of the Arts London
m.grierson@arts.ac.uk
Abstract

This paper details a developing artistic practice around an ongoing series of works called (un)stable equilibrium. These works are the product of using modern machine toolkits to train generative models without data, an approach akin to traditional generative art where dynamical systems are explored intuitively for their latent generative possibilities. We discuss some of the guiding principles that have been learnt in the process of experimentation, present details of the implementation of the first series of works and discuss possibilities for future experimentation.

1 Introduction

In this work we are utilising toolkits for data driven optimisation (such as PyTorch Paszke et al. (2017)) and pre-existing generative model architectures with strong inductive biases, to explore the latent possibilities of potential generative outputs that don’t mimic any existing data distribution. We have set out to achieve this by finding ways of ‘training’ these generative models without any training data. This approach can be seen as akin to practices in traditional generative art, where dynamic systems are built and the role of the artist is to design or influence this process to some degree, based on intuition and exploration McCormack et al. (2004). We see this as a continuation of artistic practice describe by Bense as Generative Aesthetics, where we are using the modern tools of gpu-optimised linear algebra libraries, differentiable objective functions and gradient-based optimisation to design and explore the characteristics of these new ‘aesthetic structures’ Bense (1965).

2 Guiding Principles

Here we discuss some of the useful concepts and fruitful techniques that have been learnt through the process of experimentation and utilised in the works in Series 1 (see Section 3 for details).

Complexity | Stochasticity

A lot of the trial and error in the practice is finding the right balance of complexity and stochasticity. Often finding the right batch size is key, too low and gradients quickly explode, too high and the error signal averages out any potential system dynamics, resulting in stasis.

Relational Constraints

We often utilise constraints that are relative to the output of a given batch. These constraints may be distances in embedding spaces using techniques from metric learning Kulis and others (2013), or measure of diversity in pixel space of a generators batch-wise output.

Exploiting Boundaries

We exploit the small differences in the way different differentiable functions measure distance and difference. These discrepancies can be exploited to create internal system dynamics that continually inject a level of randomness into the training dynamics.

Diametric Optimisation

In some of the arrangements for the works in Series 1, we train some of the networks in the system with two diametrically opposed loss functions (propagated after exposure to different batches to prevent them from completely cancelling out). While this may be counter-intuitive from the perspective of optimisation, it provides an anchor of stability in networks ensembles where the other networks are relying on that network’s output in the process of training.

Discovering (Un)stable Equilibria

The previous guiding principles are all techniques that serve the goal of finding a balance of randomness and stability: to find an equilibrium in the space of potential system dynamics which is stable enough to prevent gradients collapsing or exploding, but unstable enough to produce unexpected results.

3 Case Study: Series 1

For the works in Series 1, the setup resembles the popular generative adversarial networks Goodfellow et al. (2014) ensemble, however here we have two generators (both using progressively-growing, style-based generator architectures Karras et al. (2019)). The ‘discriminator’ sometimes acts in the traditional way as a binary classifier, trying to correctly classify which generator has produced which image. Alternatively, it is sometimes trained simultaneously with both diametrical opposing adversarial loss functions (this is true for the the works 1:1, 1:2 and 1:6). In either case, the discriminator’s classification output, and distance measurements in the discriminator’s embedding space are utilised for training the generators.

The generators compete in having their output as being recognized as the output of the other network, either using the classification output of the discriminator or by having the distance of their embeddings (in discriminator space) as close as possible to the other generators. Both generators also compete to have more variety in the colours they output at pixel level (in their respective batch) than the other generator. These arrangements result in abstract, sometimes orthogonal compositions from the two generators. After training is completed, the resulting images from the two generators is presented side-by-side, as a video piece showing a synchronised interpolation between their respective latent spaces (see Figures 1-6 in the Appendix for stills from and links to the video works in the series).

4 Discussion and Future Work

In this work, we have developed a practice that relies heavily on the subjective aesthetic analysis of the output. Through subjective interpretation of the output of these systems (often through closely monitoring results visually throughout training), an intuition has been developed that has informed decisions in the iterations of model design. We find this practice to resonate strongly with Stanley’s description of the role of artistic understanding in the process of researching artificial systems Stanley (2018).

In future experiments, we look to develop the practice further and explore aesthetic possibilities of other commonly used techniques in machine learning, such as the variety of diversity metrics now used to assess generative models, such as the inception score Salimans et al. (2016) and Fréchet inception distance Heusel et al. (2017). We also want to experiment with integrating meta-information of training performance into the training of the model, as well as adaptive and evolutionary techniques to dynamically change the model architectures and meta-model arrangements.

Acknowledgments

This work has been supported by UK’s EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI; grant EP/L015846/1).

References

  • [1] M. Bense (1965) Projekte generativer ästhetik. F. von Cube (Flg.), Was ist Kybernetik1 Grundbegriffe, Methoden, Anwendungen, dtv WR 4079. Cited by: §1.
  • [2] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio (2014) Generative adversarial nets. In Advances in neural information processing systems, pp. 2672–2680. Cited by: §3.
  • [3] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, pp. 6626–6637. Cited by: §4.
  • [4] T. Karras, S. Laine, and T. Aila (2019) A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401–4410. Cited by: §3.
  • [5] B. Kulis et al. (2013) Metric learning: a survey. Foundations and Trends® in Machine Learning 5 (4), pp. 287–364. Cited by: §2.
  • [6] J. McCormack, A. Dorin, T. Innocent, et al. (2004) Generative design: a paradigm for design research. Proceedings of Futureground, Design Research Society, Melbourne. Cited by: §1.
  • [7] A. Paszke, S. Gross, S. Chintala, and G. Chanan (2017) Pytorch: tensors and dynamic neural networks in python with strong gpu acceleration. PyTorch: Tensors and dynamic neural networks in Python with strong GPU acceleration 6. Cited by: §1.
  • [8] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen (2016) Improved techniques for training gans. In Advances in neural information processing systems, pp. 2234–2242. Cited by: §4.
  • [9] K. O. Stanley (2018) Art in the sciences of the artificial. Leonardo 51 (2), pp. 165–172. Cited by: §4.
Figure 1: Still from (un)stable equilibrium 1:1. The work can be viewed at: https://youtu.be/r6MB555mXXM
Figure 2: Still from (un)stable equilibrium 1:2. The work can be viewed at: https://youtu.be/P_LLD8ffgVc
Figure 3: Still from (un)stable equilibrium 1:3. The work can be viewed at: https://youtu.be/A4CsL5TAvQU
Figure 4: Still from (un)stable equilibrium 1:4. The work can be viewed at: https://youtu.be/SL69OZD_-cM
Figure 5: Still from (un)stable equilibrium 1:5. The work can be viewed at: https://youtu.be/b8X-KrO4JzM
Figure 6: Still from (un)stable equilibrium 1:6. The work can be viewed at: https://youtu.be/Jxhi3P2edVQ
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