Summary of Clip the Bias: How Useful Is Balancing Data in Multimodal Learning?, by Ibrahim Alabdulmohsin et al.
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
by Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner, Priya Goyal, Alexander D’Amour, Xiaohua Zhai
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We investigate the effectiveness of data-balancing techniques for mitigating biases in contrastive language-image pretraining (CLIP). Our analysis reaffirms previous findings that CLIP models can absorb societal stereotypes, and we propose a novel algorithm called Multi-Modal Moment Matching (M4) to reduce representation and association biases. We conduct an in-depth study using M4, considering various factors such as model architecture, representation, data size, and fine-tuning. Our results show that fine-tuning is effective for countering representation biases but has limited impact on association biases. Data balancing has a mixed effect on performance, improving classification but potentially hurting retrieval. Interestingly, architectural improvements can mitigate the negative impact of data balancing on performance. We conclude with recommendations for improving data balancing efficacy in multimodal systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make language-image learning models fairer and more accurate. The researchers found that these models can pick up biases from the world around us, which is a problem. To fix this, they created a new way to balance data called Multi-Modal Moment Matching (M4). They tested M4 with different factors in mind, like what kind of model it was and how much training data there was. The results show that making small changes to the model can help reduce biases, but bigger problems may remain. Overall, the study suggests ways to make language-image learning models fairer and more accurate. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Multi modal * Pretraining
