Adversarial Latent Autoencoders (2004.04467v1)

Abstract: Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.

Adversarial Latent Autoencoders

The paper "Adversarial Latent Autoencoders" introduces a novel architecture that effectively integrates the strengths of autoencoders (AEs) and Generative Adversarial Networks (GANs), addressing specific limitations of traditional AEs in terms of generative power and disentangled representation learning. The proposed model, termed Adversarial Latent Autoencoder (ALAE), positions itself as a significant advancement by unifying the dual capabilities of GANs and AEs, thereby enhancing image generation and manipulation tasks.

Core Contributions

The authors present ALAE as a generalized architecture that can leverage state-of-the-art GAN training methodologies to match the generative prowess of GANs while also focusing on disentangled representations. The ALAE framework comprises an encoder-generator map, which is trained adversarially. The key distinctions drawn in this work include:

• Latent Space Learning: ALAE does not impose a fixed distribution on the latent space; instead, it learns this distribution from data. This is a shift from typical approaches where the latent space is constrained to a predetermined distribution.
• Technical Novelty: The architecture ensures reciprocity in the latent space rather than the data space, avoiding the pitfalls associated with using reconstruction losses in pixel space. This method aims to achieve a better representation that supports both generation and manipulation tasks.
• StyleALAE: A variant of their model incorporates the StyleGAN generator, dubbed StyleALAE. This variation maintains high-resolution image generation capabilities, comparable in quality to StyleGAN, while enabling tasks such as image reconstruction and real-image manipulations—extending beyond what GANs typically achieve.

Experimental Insights

The ALAE framework was subjected to rigorous testing across multiple datasets, demonstrating its capability to produce high-quality reconstructions and generative outputs. The authors report experiments on MNIST, FFHQ, and LSUN Bedroom datasets, showcasing both the qualitative and quantitative strengths of their approach.

• Disentanglement and Representation: The experiments indicate that ALAE learns a more disentangled latent space compared to other methods, as evidenced by its performance in various classification and interpolation tasks.
• Image Quality Metrics: The architecture achieves competitive FID scores, although there is a noticeable gap with state-of-the-art GANs, a possible trade-off given ALAE's bidirectional capabilities.
• Image Reconstruction and Manipulation: Unlike traditional GAN-focused approaches, ALAE demonstrates proficiency in not only generating realistic synthetic images but also effectively reconstructing and manipulating real-world images.

Implications and Future Outlook

ALAE introduces a framework that could significantly impact both theoretical research and practical applications in AI, particularly in domains requiring advanced image processing and manipulation. The disentangled and learned latent space potentially facilitates more robust and interpretable representations, which are crucial for various downstream tasks such as image editing, augmented reality, and animation.

Moving forward, there are several avenues for future research:

• Optimization and Efficiency: Enhancing the computational efficiency and optimization strategies to reduce the gap between reconstruction and generation quality.
• Broader Applications: Extending the principles of ALAE to other data modalities such as audio and video, potentially delivering improvements in areas like speech synthesis and video editing.
• Integration with Other Architectures: Exploring the integration of ALAE with other emerging architectures and techniques to widen its scope and applicability further.

In conclusion, the Adversarial Latent Autoencoder introduces a significant conceptual advance in bridging the gap between autoencoding and adversarial generation, setting a foundation for further innovations in disentangled and high-quality data representations.