1 Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

id:: 2208.04202
Authors:: Ting Chen, Ruixiang Zhang, Geoffrey Hinton
Published:: 2022-08-08
arXiv:: https://arxiv.org/abs/2208.04202
PDF:: https://arxiv.org/pdf/2208.04202
DOI:: N/A
Journal Reference:: N/A
Primary Category:: cs.CV
Categories:: cs.CV, cs.AI, cs.CL, cs.LG
Comment:: ICLR’23
github_url:: _

1.1 abstract

We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which we call analog bits. To generate samples, the model first generates the analog bits, which are then thresholded to obtain the bits that represent the discrete variables. We further propose two simple techniques, namely Self-Conditioning and Asymmetric Time Intervals, which lead to a significant improvement in sample quality. Despite its simplicity, the proposed approach can achieve strong performance in both discrete image generation and image captioning tasks. For discrete image generation, we significantly improve previous state-of-the-art on both CIFAR-10 (which has 3K discrete 8-bit tokens) and ImageNet-64x64 (which has 12K discrete 8-bit tokens), outperforming the best autoregressive model in both sample quality (measured by FID) and efficiency. For image captioning on MS-COCO dataset, our approach achieves competitive results compared to autoregressive models.

1.2 premise

1.3 outline

1.4 quotes

1.5 notes

1.6 summary

1.6.1 1. Brief Overview

This paper introduces Bit Diffusion, a novel approach for generating discrete data using continuous diffusion models. The core idea involves representing discrete data (like images) as binary bits, then modeling these bits as real numbers (“analog bits”) using a continuous diffusion model. The model generates analog bits, which are then thresholded to obtain the final discrete representation. Two techniques, Self-Conditioning and Asymmetric Time Intervals, significantly improve sample quality. The approach achieves state-of-the-art results on discrete image generation tasks (CIFAR-10, ImageNet 64x64) and competitive results in image captioning (MS-COCO).

1.6.2 2. Key Points

Represents discrete data as binary bits, then models these as real numbers (analog bits) with a continuous diffusion model.
Introduces Self-Conditioning (conditioning the model on previously generated samples) to improve sample quality.
Employs Asymmetric Time Intervals during sampling for further quality enhancements.
Achieves state-of-the-art results on CIFAR-10 and ImageNet 64x64 image generation tasks.
Demonstrates competitive performance on image captioning with the MS-COCO dataset.
The approach is simple and generic, potentially applicable to various diffusion model architectures.

1.6.3 3. Notable Quotes

None explicitly stated in the abstract or introduction that warrant inclusion.

1.6.4 4. Primary Themes

Generative Modeling of Discrete Data: The primary focus is on developing effective methods for generating discrete data, a challenging problem for many continuous generative models.
Diffusion Models: The paper leverages the strengths of diffusion models, specifically their ability to handle high-dimensional data efficiently and generate high-quality samples.
Novel Techniques: The introduction of Self-Conditioning and Asymmetric Time Intervals showcases the exploration of new techniques to improve existing methods.
State-of-the-Art Results: The impressive results on benchmark datasets highlight the effectiveness of the proposed approach.