Core Concepts
Heterogeneous contrastive learning is a powerful approach to train large-scale foundation models that can effectively handle diverse data sources and tasks without relying on labeled data. By leveraging contrastive learning to model view heterogeneity and task heterogeneity, these foundation models can learn compact and high-quality representations that generalize well across a wide range of applications.
Abstract
This paper provides a comprehensive survey on the current landscape of heterogeneous contrastive learning for foundation models. It first introduces the basic concept of contrastive learning and how it can be applied to handle view heterogeneity, where data comes from multiple sources. The authors then discuss how contrastive learning is used to train large vision, language, and multimodal foundation models by leveraging data augmentation techniques to generate different views of the input.
The paper then moves on to contrastive learning for task heterogeneity, where the foundation models are trained on a diverse set of pre-training tasks, including pretext tasks, supervised tasks, preference tasks, and auxiliary tasks. These pre-training tasks inject different characteristics of the data into the model, which can then be fine-tuned on a variety of downstream tasks through strategies like automated machine learning, prompt learning, and multi-task learning.
The authors also highlight several open challenges and future research directions in this area, such as developing more efficient contrastive learning algorithms, incorporating human feedback and knowledge into the training process, and extending heterogeneous contrastive learning to other data modalities beyond vision and language.
Stats
"Recent years have witnessed the rapid growth of the volume of big data. A Forbes report shows that the amount of newly created data in the past several years had increased by more than two trillion gigabytes."
"One major characteristic of big data is heterogeneity. Specifically, big data are usually collected from multiple sources and associated with various tasks, exhibiting view or task heterogeneity."
Quotes
"Contrastive Learning (CL) has gained an increasing interest in training foundation models, due to its good generalization capability and the independence of labeled data."
"Amidst the explosive advancements in foundation models across multiple domains, including natural language processing and computer vision, there is an urgent need for a comprehensive survey on heterogeneous contrastive learning for foundational models."