Synthesizing Programs for Images using Reinforced Adversarial Learning (1804.01118v1)

Abstract: Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator's output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, Omniglot, CelebA) and synthetic 3D datasets.

• The paper introduces the SPIRAL agent that synthesizes image programs through a reinforced adversarial learning framework, significantly improving convergence over traditional pixel-based losses.
• The paper validates SPIRAL's effectiveness across varied datasets such as MNIST, CelebA, and synthetic 3D scenes, demonstrating its ability to capture structured visual representations.
• The paper employs an advanced REINFORCE-based strategy with adversarial feedback to rapidly optimize policy learning, paving the way for scalable inverse graphics and program synthesis.

An Overview of "Synthesizing Programs for Images using Reinforced Adversarial Learning"

The paper "Synthesizing Programs for Images using Reinforced Adversarial Learning," authored by Yaroslav Ganin and collaborators from the Montreal Institute for Learning Algorithms and DeepMind, presents a novel framework for image generation and understanding through program synthesis. This approach combines adversarial reinforcement learning with rendering engines to synthesize high-level visual representations, focusing specifically on the SPIRAL (Synthesizing Programs for Images using Reinforced Adversarial Learning) agent. Unlike many existing models, SPIRAL generates visual programs that graphical engines interpret to produce images, leveraging the discriminator's feedback as a reinforcement in its policy learning process. This paper's contributions are tested on a range of datasets, demonstrating efficacy and offering a scaling pathway for inverse graphics and program synthesis.

Key Contributions and Methodology

1. Adversarially Trained Agent: The SPIRAL architecture uses an adversarial setup where an unsupervised agent is trained to generate programs interpreted by a rendering simulator to produce images. This process is guided by a discriminator trained to distinguish between real and synthesized images, with the agent seeking to fool the discriminator.
2. Reinforcement Learning Framework: The agent is optimized using an advanced variant of the REINFORCE algorithm integrated into a distributed framework, following a strategy that includes multiple asynchronous actors and learners, inspired by the IMPALA architecture. Through policy gradients, the agent receives rewards based on the discriminator's evaluation of its outputs.
3. Scalability and Domain Agnosticism: SPIRAL is notably scalable—demonstrated on datasets like MNIST, Omniglot, CelebA, and synthetic 3D scenes—and agnostic to nuances of visual programs or target domains, requiring no additional supervision beyond the adversarial framework.
4. Superior Learning Signals from Discriminators: It is reported that utilizing the discriminator's learned output as a reward leads to faster training convergence and better optimization than traditional pixel-wise losses like l2 distance, especially in tasks demanding complex inference.

Empirical Results and Analysis

The authors conducted extensive experiments validating the SPIRAL framework's applicability across various image domains. Notably:

• MNIST and Omniglot: SPIRAL learned to generate and reconstruct handwritten digits and characters with traceable stroke sequences, indicating structure comprehension beyond mere pixel alignment.
• CelebA: On more complex datasets, the agent synthesized plausible albeit abstract facial images, highlighting semantic understanding even with limited coherence at a pixel level.
• MuJoCo Scenes: For synthetic 3D scenes, SPIRAL reconstructed scenes with high similarity to target images, outperforming alternatives like Metropolis-Hastings inference in complex attribute spaces.

Implications and Future Work

The SPIRAL agent stands as a significant step towards scalable visual program synthesis, with implications for fields like graphics, computer vision, and AI-driven creative applications. Its use of the Wasserstein GAN framework for adversarial training could inspire similar adaptations across different domains involving unsupervised complex structure learning. Furthermore, the methodology paves the way for integrating more sophisticated exploration and search strategies, like Monte Carlo Tree Search, which could significantly enhance program synthesis capabilities.

The work brings to light several future directions, such as optimizing the parameterization of action spaces and exploring more structured representation forms, that may lead to improvements in rendering fidelity and semantic understanding. There is also potential in leveraging joint image-action discriminators to provide richer learning signals to the agent, suggesting broader research implications for domain adaptation and transfer learning.

In conclusion, the SPIRAL method's potential to advance the state-of-the-art in inverse graphics and program synthesis is evident, offering promising avenues for further development and application across diverse computational domains.