Sample what you cant compress (2409.02529v3)

Abstract: For learned image representations, basic autoencoders often produce blurry results. Reconstruction quality can be improved by incorporating additional penalties such as adversarial (GAN) and perceptual losses. Arguably, these approaches lack a principled interpretation. Concurrently, in generative settings diffusion has demonstrated a remarkable ability to create crisp, high quality results and has solid theoretical underpinnings (from variational inference to direct study as the Fisher Divergence). Our work combines autoencoder representation learning with diffusion and is, to our knowledge, the first to demonstrate the efficacy of jointly learning a continuous encoder and decoder under a diffusion-based loss. We demonstrate that this approach yields better reconstruction quality as compared to GAN-based autoencoders while being easier to tune. We also show that the resulting representation is easier to model with a latent diffusion model as compared to the representation obtained from a state-of-the-art GAN-based loss. Since our decoder is stochastic, it can generate details not encoded in the otherwise deterministic latent representation; we therefore name our approach "Sample what you can't compress", or SWYCC for short.

• The paper introduces a novel diffusion-based loss in autoencoders, significantly enhancing image reconstruction fidelity compared to traditional GAN approaches.
• It combines a continuous encoder with a U-Net diffusion decoder, leveraging auxiliary perceptual and MSE losses to improve training and output quality.
• Experimental results demonstrate lower distortion levels and improved latent space modeling, advancing both reconstruction and generative performance in image processing.

Overview of "Sample What You Can't Compress"

The paper "Sample What You Can't Compress" (SWYCC) presents a novel approach to image autoencoder design by integrating diffusion-based techniques with traditional encoder-decoder architectures. This research explores the limitations of traditional autoencoders, specifically those using GAN-based methods, and proposes an alternative using a diffusion loss to improve reconstruction quality and sampling diversity.

Methodology

The authors introduce a diffusion-based loss function applied within the autoencoder framework. Diffusion models, known for their capability in generating high-quality images with well-defined theoretical properties, form the core of this autoencoder. The proposed method involves:

• Continuous Encoder-Decoder Learning: By jointly learning a continuous encoder and decoder using diffusion-based loss, the model samples image details that are not explicitly encoded in deterministic latent representations.
• Architecture: The architecture combines a traditional encoder with a U-Net-based diffusion model as the decoder. This design offers better reconstruction fidelity compared to GAN-based methods, as it can incorporate stochastic elements during decoding.
• Auxiliary Losses: The paper emphasizes the importance of incorporating perceptual and MSE losses to accelerate training and improve image quality. The perceptual loss, originating from a pre-trained ResNet, impacts substantially upon the final reconstruction output.

Experimental Results

The experiments conducted show substantial improvements using SWYCC over state-of-the-art GAN-based autoencoders. Notably:

• Reconstruction Quality: The model achieves lower distortion levels across various compression ratios, as measured by the Continuous Math Model Distortion (CMMD) metric. This suggests that SWYCC retains image quality better than GAN-based counterparts, especially at high compression levels.
• Latent Space Modeling: The latent space representations derived from SWYCC improve subsequent diffusion model training for class-conditional image generation tasks, achieving a 5% reduction in Fréchet Inception Distance (FID).

Implications and Future Directions

This research implies significant advancements in both reconstructive and generative capabilities of autoencoders:

• Theoretical Contribution: The research aligns with recent theoretical advances in diffusion models, providing a principled approach to image reconstruction distinct from GAN-based models' empirical designs.
• Practical Applications: The proposed architecture can extend beyond images to other continuous modalities, such as audio and point clouds, offering a new direction for compression and generation in multimedia applications.
• Sampling Efficiency: While the diffusion model enhances reconstruction quality, the increased computational cost due to iterative sampling remains a challenge. Future work may focus on optimizing diffusion sampling strategies, possibly through distillation techniques.

Conclusion

The paper presents a compelling case for utilizing diffusion processes in autoencoders, promising enhanced reconstruction quality and a more flexible latent space. The SWYCC method reflects a shift towards principled, theoretically grounded approaches in image modeling, encouraging the exploration of more efficient, high-quality generative models in machine learning.