Google llc (20240104352). Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training simplified abstract

Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training

Organization Name

google llc

Inventor(s)

Yu Zhang of Mountain View CA (US)

Yu-An Chung of Mountain View CA (US)

Wei Han of Mountain View CA (US)

Chung-Cheng Chiu of Mountain View CA (US)

Weikeng Qin of Sunnyvale CA (US)

Ruoming Pang of New York NY (US)

Yonghui Wu of Palo Alto CA (US)

Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104352 titled 'Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training

Simplified Explanation

The patent application describes an improved end-to-end self-supervised pre-training framework that combines contrastive learning and masked modeling to optimize a model in an end-to-end fashion.

• The framework leverages a combination of contrastive and masked modeling loss terms.
• Contrastive learning discretizes input data into discriminative tokens, while masked modeling trains the model to learn contextualized representations via a masked prediction task.
• Unlike existing frameworks that rely on iterative re-clustering or concatenating separately trained modules, this framework solves the contrastive task and masked modeling task simultaneously.

Potential Applications

This technology could be applied in various fields such as natural language processing, speech recognition, and computer vision for improving pre-training models.

Problems Solved

1. Optimizing models in an end-to-end fashion. 2. Enhancing self-supervised pre-training frameworks by combining contrastive learning and masked modeling.

Benefits

1. Improved model performance. 2. Simultaneous optimization of self-supervised tasks. 3. Enhanced contextualized representations.

Potential Commercial Applications

The technology could be utilized in industries such as healthcare, finance, and e-commerce for tasks like data analysis, customer service automation, and fraud detection.

Possible Prior Art

One possible prior art could be existing self-supervised pre-training frameworks that focus on contrastive learning or masked modeling separately, rather than combining the two tasks in an end-to-end fashion.

Unanswered Questions

How does this framework compare to other self-supervised pre-training methods in terms of performance and efficiency?

Further comparative studies are needed to evaluate the effectiveness of this framework against existing methods.

Are there any limitations or challenges in implementing this framework in real-world applications?

Exploring potential obstacles or constraints in deploying this technology in practical settings would be beneficial for understanding its feasibility.

Original Abstract Submitted

provided are improved end-to-end self-supervised pre-training frameworks that leverage a combination of contrastive and masked modeling loss terms. in particular, the present disclosure provides framework that combines contrastive learning and masked modeling, where the former trains the model to discretize input data (e.g., continuous signals such as continuous speech signals) into a finite set of discriminative tokens, and the latter trains the model to learn contextualized representations via solving a masked prediction task consuming the discretized tokens. in contrast to certain existing masked modeling-based pre-training frameworks which rely on an iterative re-clustering and re-training process or other existing frameworks which concatenate two separately trained modules, the proposed framework can enable a model to be optimized in an end-to-end fashion by solving the two self-supervised tasks (the contrastive task and masked modeling) simultaneously.