Adversarial Feature Learning (1605.09782v7)

Abstract: The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.

• The paper introduces a novel unsupervised framework, BiGANs, that integrates an encoder with GANs to map data back to latent space.
• The method enforces that the encoder and generator function as effective inverses, supported by theoretical guarantees and results on MNIST and ImageNet.
• Transfer learning experiments demonstrate that BiGANs deliver robust feature representations for tasks such as ImageNet and PASCAL VOC classification, detection, and segmentation.

An Expert Overview of "Adversarial Feature Learning"

The paper "Adversarial Feature Learning" authored by Donahue, Krähenbühl, and Darrell proposes a novel framework for unsupervised feature learning called Bidirectional Generative Adversarial Networks (BiGANs). This work builds upon the existing framework of Generative Adversarial Networks (GANs), addressing a significant limitation by introducing an encoder to learn the inverse mapping from data to latent space.

Introduction

The paper begins by highlighting the utility of deep convolutional networks across various perceptual domains such as computer vision, natural language processing, and speech recognition. While these networks have achieved impressive results, they rely on supervisory signals from large-scale, hand-labeled datasets. The authors then pivot to GANs, a class of models that learn to generate data distributions from simple latent distributions in an unsupervised manner. GANs have shown promising results in generating high-quality samples of complex data distributions, as well as in capturing semantic variations in the latent space.

However, a notable shortcoming of GANs lies in their inability to map data back to the latent space, which limits their use in feature representation tasks. The authors propose BiGANs to overcome this gap. BiGANs employ an encoder alongside the generator to learn this inverse mapping, thereby enabling the model to be useful for feature learning in an unsupervised context.

Methodology

Bidirectional Generative Adversarial Networks

The core innovation in BiGANs is the introduction of an encoder $E$ that complements the traditional GAN generator $G$. While the generator creates data samples from latent variables, the encoder projects data samples back into the latent space. The discriminator $D$ in a BiGAN is modified to distinguish between joint pairs of data and latent variables (i.e., $(x, E(x))$ versus $(G(z), z)$). The minimax objective for training BiGANs is thereby extended to include both the generator and the encoder.

BiGANs are shown to enforce that the encoder and generator functions approximate each other's inverses, which is critical for encoding accurate semantic feature representations. The paper provides rigorous theoretical guarantees, proving that at the global optimum, these two functions are indeed inverses of each other.

Training Procedure and Optimization

Training BiGANs involves alternating updates to the parameters of the discriminator, generator, and encoder using stochastic gradient descent. The paper notes that a variant objective function can provide stronger gradient signals, thereby enhancing convergence. Each module $D$, $E$, and $G$ is implemented as a deep neural network, typically a convolutional network.

Evaluation

The paper evaluates BiGANs through unsupervised feature learning tasks on permutation-invariant MNIST and high-resolution ImageNet datasets. The results on MNIST show that BiGANs perform comparably to traditional autoencoders and other GAN-based methods in terms of nearest neighbor classification accuracy. For ImageNet, qualitative analyses present samples generated by BiGANs, reconstructions from these samples, and learned convolutional filters that highlight their effectiveness in learning robust features.

Moreover, transfer learning experiments demonstrate the versatility and robustness of BiGAN representations. When transferred to ImageNet classification, and PASCAL VOC classification, detection, and segmentation tasks, BiGANs showcase competitive performance against contemporary self-supervised methods.

Implications and Future Directions

BiGANs present a generalizable approach to unsupervised feature learning, removing the necessity for domain-specific modifications. This framework is particularly promising for tasks where supervisory signals are scarce or unavailable. Given their demonstrated capability and theoretical foundations, BiGANs are robust competitors to sophisticated self-supervised approaches that exploit domain-specific cues, such as context prediction and video sequence learning.

Future research can explore enhancements in the structural design of the generator and encoder for better image synthesis and feature learning. Additionally, expanding this framework to other data types and domains like text and audio could be a potential direction, leveraging the encoder-generator pair's ability to learn effective representations in a purely unsupervised manner.

Conclusion

The paper introduces Bidirectional Generative Adversarial Networks (BiGANs), a powerful framework for unsupervised feature learning that addresses a critical gap in traditional GANs. Through the introduction of an encoder, BiGANs successfully map data back to latent space, providing meaningful feature representations. The theoretical and empirical analyses convincingly demonstrate the utility and effectiveness of BiGANs across diverse datasets and tasks, laying the groundwork for future advancements in unsupervised learning methodologies.