MoDE: CLIP Data Experts via Clustering
arXiv preprint arXiv:2404.16030
Published On 2024/4/24
The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (35\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode.
Journal
arXiv preprint arXiv:2404.16030
Authors
Shih-Fu Chang
Columbia University in the City of New York
H-Index
134
Research Interests
Multimedia
Computer Vision
Machine Learning
Signal Processing
Information Retrieval
University Profile Page
Luke Zettlemoyer
University of Washington
H-Index
100
Research Interests
Natural Language Processing
Semantics
Machine Learning
Artificial Intelligence
University Profile Page
Po-Yao (Bernie) Huang
Carnegie Mellon University
H-Index
23
Research Interests
Multimodal machine learning
Multi-modal learning
natural language processing
University Profile Page
Jiawei Phoenix MA
Columbia University in the City of New York
H-Index
14
Research Interests
Data-Centric AI
De-Centralized AI
Reliable Life-Long Learning
Multi-Modal
Computer Vision
University Profile Page
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Jiawei Phoenix MA
Columbia University in the City of New York
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The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (35\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode.
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Columbia University in the City of New York
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University of Washington
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