Stacked Generative Adversarial Networks (1612.04357v4)

Abstract: In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.

• The paper introduces a stacked generative architecture that decomposes image variations across hierarchical levels for refined output.
• It employs representation discriminators along with conditional and entropy losses to enforce semantically rich and manifold-aligned representations.
• Evaluation on datasets like CIFAR-10 shows superior performance with an Inception score of 8.59, validating the model's effectiveness.

Stacked Generative Adversarial Networks

The paper introduces a novel generative model termed Stacked Generative Adversarial Networks (SGAN). This model aims to leverage the hierarchical representations of pre-trained discriminative networks by using a top-down stack of GANs. Unlike traditional GANs, which rely on a single noise vector to account for the variations in generated samples, SGAN employs multiple GANs to decompose variations across different representational levels. This multi-GAN architecture allows for refining uncertainties in the generative process, resulting in higher quality image generation.

Technical Contributions

SGAN is structured around a series of key innovations:

• Stacked Generative Architecture: Each GAN in the stack is responsible for generating lower-level representations from higher-level abstract ones, aligning with the representation hierarchy from the discriminative network. This unique design fosters an efficient decomposition of variation, enabling improved model outputs.
• Representation Discriminators: The paper introduces representation discriminators at various feature hierarchies. These discriminators ensure that the representations generated by the model align with those in the pre-trained discriminative network. This essay focuses on incentivizing the generators to produce plausible and semantically rich representations that lie on the manifold of the feature space.
• Conditional and Entropy Losses: The SGAN framework incorporates a conditional loss to anchor the generated lower-level representations to conditional information from the higher levels. Additionally, the entropy loss maximizes a variational lower bound on the conditional entropy, addressing one of the common pitfalls in conditional GANs where the noise vector is often neglected.

These contributions collectively advance the capability of generative models to better approximate complex data distributions.

Evaluation and Results

SGAN is rigorously evaluated on prominent datasets such as MNIST, SVHN, and CIFAR-10, demonstrating superior image quality as assessed through Inception scores and Visual Turing tests. Notably, the model achieves an Inception score of 8.59 on CIFAR-10, surpassing prior state-of-the-art results. This strong performance underscores the model's ability to generate images with both high quality and diversity.

Implications and Future Directions

The implications of SGAN are significant for both theoretical understanding and practical applications in generative modeling. The hierarchical decomposition of variations in image generation could inform future architectures that require fine-grained control over generative factors, such as style transfer and domain adaptation.

Looking forward, the approach could be extended to unsupervised scenarios, potentially broadening its applicability. The entropy maximization strategy introduced in SGAN could motivate further exploration within other areas where output diversity is critical, such as economic modeling or synthetic data generation for privacy-preserving data analysis.

In conclusion, the SGAN model enriches the GAN paradigm by effectively utilizing hierarchical structures and improving the interpretability of generative models. Its methodological advancements exhibit promising directions for building more sophisticated and capable AI systems in the field of generative modeling.