Google llc (20240112088). Vector-Quantized Image Modeling simplified abstract

Vector-Quantized Image Modeling

Organization Name

google llc

Inventor(s)

Jiahui Yu of Jersey City NJ (US)

Xin Li of Santa Clara CA (US)

Han Zhang of Sunnyvale CA (US)

Vijay Vasudevan of Los Altos Hills CA (US)

Alexander Yeong-Shiuh Ku of Brooklyn NY (US)

Jason Michael Baldridge of Austin TX (US)

Yuanzhong Xu of Mountain View CA (US)

Jing Yu Koh of Austin TX (US)

Thang Minh Luong of Santa Clara CA (US)

Gunjan Baid of San Francisco CA (US)

Zirui Wang of San Francisco CA (US)

Yonghui Wu of Palo Alto CA (US)

Vector-Quantized Image Modeling - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112088 titled 'Vector-Quantized Image Modeling

Simplified Explanation

The present disclosure describes a system and method for vector-quantized image modeling using vision transformers and improved codebook handling. This approach involves pretraining a machine learning model, such as a transformer model, to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned vision-transformer-based VQGAN, referred to as VIT-VQGAN, which includes multiple improvements over vanilla VQGAN from architecture to codebook learning, resulting in better efficiency and reconstruction fidelity.

• Improved vector-quantized image modeling using vision transformers and enhanced codebook handling
• Pretraining a machine learning model to predict rasterized image tokens autoregressively
• Encoding discrete image tokens from a learned vision-transformer-based VQGAN (VIT-VQGAN)
• Multiple improvements over vanilla VQGAN from architecture to codebook learning
• Better efficiency and reconstruction fidelity in vector-quantized image modeling tasks

Potential Applications

The technology described in the patent application can be applied in various fields such as:

• Image generation
• Class-conditioned image generation
• Unsupervised representation learning

Problems Solved

The technology addresses the following issues:

• Efficient vector-quantized image modeling
• Improved reconstruction fidelity in image generation tasks

Benefits

The benefits of this technology include:

• Enhanced efficiency in image modeling
• Better reconstruction fidelity in image generation tasks

Potential Commercial Applications

The technology can have potential commercial applications in industries such as:

• Entertainment (e.g., video game development, special effects)
• Advertising (e.g., personalized content generation)
• E-commerce (e.g., product visualization)

Possible Prior Art

One possible prior art in this field is the original VQGAN model, which laid the foundation for vector-quantized image modeling using neural networks.

What are the specific improvements made to the codebook learning process in the proposed VIT-VQGAN model?

The abstract mentions improvements in codebook learning, but it does not provide specific details on the enhancements made in this aspect of the model.

How does the proposed technology compare to existing methods in terms of computational efficiency?

While the abstract mentions better efficiency, it does not elaborate on how the proposed approach compares to other methods in terms of computational resources and speed.

Original Abstract Submitted

systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. in particular, the present disclosure provides a vector-quantized image modeling (vim) approach that involves pretraining a machine learning model (e.g., transformer model) to predict rasterized image tokens autoregressively. the discrete image tokens can be encoded from a learned vision-transformer-based vqgan (example implementations of which can be referred to as vit-vqgan). the present disclosure proposes multiple improvements over vanilla vqgan from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. the improved vit-vqgan further improves vector-quantized image modeling tasks, including unconditional image generation, conditioned image generation (e.g., class-conditioned image generation), and unsupervised representation learning.