MoDE: CLIP Data Experts via Clustering
Authors: name: Jiawei Ma, name: Po-Yao Huang, name: Saining Xie, name: Shang-Wen Li, name: Luke Zettlemoyer, name: Shih-Fu Chang, name: Wen-Tau Yih, name: Hu Xu

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.