A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI
- URL: http://arxiv.org/abs/2501.04641v1
- Date: Wed, 08 Jan 2025 17:47:06 GMT
- Title: A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI
- Authors: Kazusato Oko, Licong Lin, Yuhang Cai, Song Mei,
- Abstract summary: Multi-modal generative AI systems rely on contrastive pre-training to learn representations across different modalities.
This paper develops a theoretical framework to explain the success of contrastive pre-training in downstream tasks.
- Score: 18.974297347310287
- License:
- Abstract: Multi-modal generative AI systems, such as those combining vision and language, rely on contrastive pre-training to learn representations across different modalities. While their practical benefits are widely acknowledged, a rigorous theoretical understanding of the contrastive pre-training framework remains limited. This paper develops a theoretical framework to explain the success of contrastive pre-training in downstream tasks, such as zero-shot classification, conditional diffusion models, and vision-language models. We introduce the concept of approximate sufficient statistics, a generalization of the classical sufficient statistics, and show that near-minimizers of the contrastive pre-training loss are approximately sufficient, making them adaptable to diverse downstream tasks. We further propose the Joint Generative Hierarchical Model for the joint distribution of images and text, showing that transformers can efficiently approximate relevant functions within this model via belief propagation. Building on this framework, we derive sample complexity guarantees for multi-modal learning based on contrastive pre-trained representations. Numerical simulations validate these theoretical findings, demonstrating the strong generalization performance of contrastively pre-trained transformers in various multi-modal tasks.
Related papers
- Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning [7.412307614007383]
Multimodal learning models are designed to bridge different modalities, such as images and text, by learning a shared representation space.
These models often exhibit a modality gap, where different modalities occupy distinct regions within the shared representation space.
We identify the critical roles of mismatched data pairs and a learnable temperature parameter in causing and perpetuating the modality gap during training.
arXiv Detail & Related papers (2024-12-10T20:36:49Z) - On the Comparison between Multi-modal and Single-modal Contrastive Learning [50.74988548106031]
We introduce a theoretical foundation for understanding the differences between multi-modal and single-modal contrastive learning.
We identify the critical factor, which is the signal-to-noise ratio (SNR), that impacts the generalizability in downstream tasks of both multi-modal and single-modal contrastive learning.
Our analysis provides a unified framework that can characterize the optimization and generalization of both single-modal and multi-modal contrastive learning.
arXiv Detail & Related papers (2024-11-05T06:21:17Z) - Revealing Multimodal Contrastive Representation Learning through Latent
Partial Causal Models [85.67870425656368]
We introduce a unified causal model specifically designed for multimodal data.
We show that multimodal contrastive representation learning excels at identifying latent coupled variables.
Experiments demonstrate the robustness of our findings, even when the assumptions are violated.
arXiv Detail & Related papers (2024-02-09T07:18:06Z) - Concept Learning for Interpretable Multi-Agent Reinforcement Learning [5.179808182296037]
We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning.
This allows an expert to both reason about the resulting concept policy models in terms of these high-level concepts at run-time, as well as intervene and correct mispredictions to improve performance.
We show that this yields improved interpretability and training stability, with benefits to policy performance and sample efficiency in a simulated and real-world cooperative-competitive multi-agent game.
arXiv Detail & Related papers (2023-02-23T18:53:09Z) - Synergies between Disentanglement and Sparsity: Generalization and
Identifiability in Multi-Task Learning [79.83792914684985]
We prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations.
Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem.
arXiv Detail & Related papers (2022-11-26T21:02:09Z) - Contrastive Learning for Fair Representations [50.95604482330149]
Trained classification models can unintentionally lead to biased representations and predictions.
Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise.
We propose a method for mitigating bias by incorporating contrastive learning, in which instances sharing the same class label are encouraged to have similar representations.
arXiv Detail & Related papers (2021-09-22T10:47:51Z) - Video Prediction via Example Guidance [156.08546987158616]
In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics.
In this work, we propose a simple yet effective framework that can efficiently predict plausible future states.
arXiv Detail & Related papers (2020-07-03T14:57:24Z) - Learning Diverse Representations for Fast Adaptation to Distribution
Shift [78.83747601814669]
We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task.
We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
arXiv Detail & Related papers (2020-06-12T12:23:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.