Inverting The Generator Of A Generative Adversarial Network (1611.05644v1)

Abstract: Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative network. When the high-dimensional distribution describes images of a particular data set, the network should learn to generate visually similar image samples for latent variables that are close to each other in the latent space. For tasks such as image retrieval and image classification, it may be useful to exploit the arrangement of the latent space by projecting images into it, and using this as a representation for discriminative tasks. GANs often consist of multiple layers of non-linear computations, making them very difficult to invert. This paper introduces techniques for projecting image samples into the latent space using any pre-trained GAN, provided that the computational graph is available. We evaluate these techniques on both MNIST digits and Omniglot handwritten characters. In the case of MNIST digits, we show that projections into the latent space maintain information about the style and the identity of the digit. In the case of Omniglot characters, we show that even characters from alphabets that have not been seen during training may be projected well into the latent space; this suggests that this approach may have applications in one-shot learning.

Inverting The Generator Of A Generative Adversarial Network

The paper "Inverting The Generator Of A Generative Adversarial Network" presents an innovative approach to addressing the challenge of inverting the generator of a pre-trained Generative Adversarial Network (GAN). This paper focuses on leveraging the learned latent space of a GAN for tasks such as image retrieval and classification. The central contribution is a methodology for mapping images back into the latent space, effectively inverting the generative process. This work has significant implications for understanding GANs and enhancing their applicability in various discriminative tasks.

Overview and Contributions

The cornerstone of the methodology involves the development of a process to infer the latent vector $z$ for an image $x$ such that when $z$ is passed through the generator $G$, it yields an image visually similar to $x$. The process of inversion is framed as a minimization problem, utilizing gradient descent to solve for $z$. Importantly, the research introduces a framework that does not necessitate additional network training, making it applicable to any pre-trained GAN model with an available computational graph.

Key contributions of the paper include:

• Inference of Latent Representations: The authors propose an inversion technique that maintains the style and identity of images, which is demonstrated on the MNIST and Omniglot datasets. In particular, their approach outperforms prior methods in terms of preserving the unique characteristics of the original image.
• Efficient Batch Inversion: By utilizing batch processing for inversion, the approach not only handles the challenges posed by batch normalization in GANs but also enhances computational efficiency, allowing multiple images to be inverted simultaneously.
• Exploration of Regularization and Prior Distributions: The paper examines the necessity of regularizing inferred latent vectors according to the prior distribution used during GAN training. The results indicate that regularization may not be critical, offering flexibility across generative models with different priors.

Results and Evaluation

The results obtained from experiments on MNIST and Omniglot datasets underscore the effectiveness of the proposed inversion method. For MNIST, the technique demonstrates superior performance in reconstructing digits while retaining both style and identity, with absolute mean reconstruction errors indicating minimal difference between use and non-use of regularization. In the case of Omniglot, which presents a more challenging scenario due to the use of characters from untrained alphabets, the inversion successfully reconstructs fine details, suggesting robust generalization capabilities. Numerical evaluation alongside qualitative analysis highlights the approach's capability to produce coherent reconstructions without the necessity for regularization.

Implications and Future Directions

The implications of this research are manifold. Practically, the ability to map images into a GAN's latent space opens up novel opportunities for leveraging generative models in image retrieval, classification, and potentially beyond, such as in the field of one-shot learning. Theoretically, understanding latent space representations and their reconstructions offers insights into the internal workings and learned distributions of GANs, potentially guiding future advancements in generative model architectures.

From a future perspective, this work prompts further exploration into the properties of learned latent spaces across diverse GAN architectures and datasets. Additionally, an investigation into the potential for enhancing inversion techniques with advances in optimization and incorporating domain-specific information could bolster the practical utility of GANs in complex real-world scenarios.

Overall, this paper provides a significant step towards demystifying GAN generators and rendering their latent spaces more accessible and utilizable for varied applications. As the field progresses, the approaches outlined could serve as a foundation for advancing both the theory and practice of generative models.