Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Attentive VQ-VAE

Angello Hoyos, Mariano Rivera

TL;DR

The paper addresses limitations of traditional VQ-VAE in capturing fine-grained textures and global consistency. It introduces the Attentive Residual Encoder (AREN) and Residual Pixel Attention to enable multi-level encoding with a parameter-efficient attention mechanism. Through experiments on CelebA-HQ, the Attentive VQ-VAE achieves competitive reconstruction quality and improved facial symmetry, while enabling robust applications such as blind restoration, denoising, and deblurring, aided by a GAN-based training approach. The work suggests broad potential for applying Attentive VQ-VAE to higher-resolution images and diverse domains, with future research exploring combined attention-hierarchy strategies and extended applications.

Abstract

We present a novel approach to enhance the capabilities of VQ-VAE models through the integration of a Residual Encoder and a Residual Pixel Attention layer, named Attentive Residual Encoder (AREN). The objective of our research is to improve the performance of VQ-VAE while maintaining practical parameter levels. The AREN encoder is designed to operate effectively at multiple levels, accommodating diverse architectural complexities. The key innovation is the integration of an inter-pixel auto-attention mechanism into the AREN encoder. This approach allows us to efficiently capture and utilize contextual information across latent vectors. Additionally, our models uses additional encoding levels to further enhance the model's representational power. Our attention layer employs a minimal parameter approach, ensuring that latent vectors are modified only when pertinent information from other pixels is available. Experimental results demonstrate that our proposed modifications lead to significant improvements in data representation and generation, making VQ-VAEs even more suitable for a wide range of applications as the presented.

Attentive VQ-VAE

TL;DR

The paper addresses limitations of traditional VQ-VAE in capturing fine-grained textures and global consistency. It introduces the Attentive Residual Encoder (AREN) and Residual Pixel Attention to enable multi-level encoding with a parameter-efficient attention mechanism. Through experiments on CelebA-HQ, the Attentive VQ-VAE achieves competitive reconstruction quality and improved facial symmetry, while enabling robust applications such as blind restoration, denoising, and deblurring, aided by a GAN-based training approach. The work suggests broad potential for applying Attentive VQ-VAE to higher-resolution images and diverse domains, with future research exploring combined attention-hierarchy strategies and extended applications.

Abstract

We present a novel approach to enhance the capabilities of VQ-VAE models through the integration of a Residual Encoder and a Residual Pixel Attention layer, named Attentive Residual Encoder (AREN). The objective of our research is to improve the performance of VQ-VAE while maintaining practical parameter levels. The AREN encoder is designed to operate effectively at multiple levels, accommodating diverse architectural complexities. The key innovation is the integration of an inter-pixel auto-attention mechanism into the AREN encoder. This approach allows us to efficiently capture and utilize contextual information across latent vectors. Additionally, our models uses additional encoding levels to further enhance the model's representational power. Our attention layer employs a minimal parameter approach, ensuring that latent vectors are modified only when pertinent information from other pixels is available. Experimental results demonstrate that our proposed modifications lead to significant improvements in data representation and generation, making VQ-VAEs even more suitable for a wide range of applications as the presented.
Paper Structure (10 sections, 3 equations, 7 figures, 5 tables, 1 algorithm)

This paper contains 10 sections, 3 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Attentive Residual Encoder
  5. Residual Pixel Attention
  6. Implementation Details
  7. Experiments
  8. Comparative Results
  9. Additional applications
  10. Conclusion

Figures (7)

  • Figure 1: General scheme of a VQ--VAE
  • Figure 2: Attentive Residual Encoder (AREN).
  • Figure 3: Modules of AREN: (a) Residual Encoder, and (b) Self--attention module.
  • Figure 4: (a) Random CelebA-HQ images $256\times256$ pixels, and their reconstructions computer with the proposed method: (b) Hierarchical without attention and two encoding levels; (c) Attentive--Hierarchical VQ-VAE with two encoding levels; and (d) Attentive VQ-VAE with one encoding level.
  • Figure 5: An example of test image in CelebA-HQ dataset with source image having blind mask effect (left), its Attentive VQ-VAE reconstructions (center) compared to the original image (right), with $40\%$ (up), $50\%$ (middle), $60\%$ (down) blind pixels.
  • ...and 2 more figures