Diffusion Autoencoders: Toward a Meaningful and Decodable Representation (2111.15640v3)

Abstract: Diffusion probabilistic models (DPMs) have achieved remarkable quality in image generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack semantic meaning and cannot serve as a useful representation for other tasks. This paper explores the possibility of using DPMs for representation learning and seeks to extract a meaningful and decodable representation of an input image via autoencoding. Our key idea is to use a learnable encoder for discovering the high-level semantics, and a DPM as the decoder for modeling the remaining stochastic variations. Our method can encode any image into a two-part latent code, where the first part is semantically meaningful and linear, and the second part captures stochastic details, allowing near-exact reconstruction. This capability enables challenging applications that currently foil GAN-based methods, such as attribute manipulation on real images. We also show that this two-level encoding improves denoising efficiency and naturally facilitates various downstream tasks including few-shot conditional sampling. Please visit our project page: https://Diff-AE.github.io/

• The paper introduces a diffusion autoencoder that separates semantic information from stochastic details, enabling high-fidelity image reconstruction.
• The paper demonstrates smooth latent space interpolation and efficient attribute manipulation without the need for optimization-based inversion.
• The paper achieves competitive autoencoding quality and sampling efficiency, advancing both theoretical understanding and practical generative modeling.

An Analysis of "Diffusion Autoencoders: Toward a Meaningful and Decodable Representation"

The paper "Diffusion Autoencoders: Toward a Meaningful and Decodable Representation" makes a significant contribution to the ongoing exploration of diffusion probabilistic models (DPMs) and their application in representation learning. The authors investigate whether DPMs, known for generating high-quality images, can also be a potent tool for learning meaningful and decodable representations, a challenge that many existing generative models struggle with.

Key Innovations

The central innovation of this work is the introduction of a diffusion autoencoder that combines a learnable encoder with a diffusion model to separate out high-level semantic content from stochastic details in images. The architecture is composed of:

• A semantic encoder that captures the semantic information of the input image, generating a compact and meaningful representation.
• A conditional Denoising Diffusion Implicit Model (DDIM) functioning as both a "stochastic encoder" and decoder. This component handles low-level stochastic details in a way that facilitates near-exact image reconstruction.

This approach allows the encoding of images into a two-part latent code, where one part captures semantics and the other captures stochastic variations. The semantic part is linear and compact, fostering efficient image reconstruction and meaningful manipulation tasks.

Experimental Results

From the experiments, it is evident that the proposed diffusion autoencoder achieves several impressive outcomes:

1. Attribute Manipulation: The model adeptly modifies specific image attributes (e.g., age, emotion) without the need for optimization-based inversion, as typically required in GAN-based methods.
2. Image Interpolation: It exhibits smoother interpolation in the latent space compared to existing diffusion models and GANs. This result signifies its potential in tasks involving continuous transformations between images.
3. Autoencoding Quality: The model demonstrates competitive reconstruction quality through its innovative two-level encoding scheme, holding its ground against leading autoencoding techniques such as NVAE and VQ-VAE2.
4. Sampling Efficiency: It achieves competitive Frechet Inception Distance (FID) scores in unconditional generation tasks, with noticeable efficiency improvements due to semantic conditioning during the denoising process.

Theoretical and Practical Implications

Theoretically, the introduction of a two-part latent code structure shifts the paradigm of how image representations are considered in diffusion models. The proposed scheme effectively decouples semantic attributes from stochastic variations, thus offering a model more aligned with human cognitive processes in image perception. Practically, this signifies a step forward in designing systems for high-fidelity image editing, where minor details need preservation while larger semantic content is altered.

Future Research Directions

The findings of this paper open up future research opportunities, including but not limited to:

• Extending the approach to other domains beyond images, such as sound or 3D point clouds, which could benefit from similar high-level semantic decompositions.
• Integrating spatial latent variables for tasks that require finer local adjustments could enhance the autoencoder's applicability in detailed scene understanding and manipulation tasks.
• Improving generation speed further toward the efficiency of GANs remains a critical challenge, demanding more sophisticated hierarchical models or accelerated training methodologies.

In conclusion, the diffusion autoencoder framework presented by Preechakul et al. provides a versatile and effective mechanism for learning decodable and interpretable image representations, contributing valuable insights to both the theoretical and practical realms of generative modeling and representation learning.