CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner, Priya Goyal,, Alexander D'Amour, Xiaohua Zhai
TL;DR
This paper evaluates the impact of data balancing on mitigating biases in CLIP models, introduces a new bias reduction algorithm M4, and analyzes its effects on model performance and bias mitigation strategies.
Contribution
The paper presents M4, a novel algorithm for reducing biases in multimodal models, and provides an in-depth analysis of data balancing effects on CLIP's bias and performance.
Findings
Data balancing improves classification but may hurt retrieval.
Fine-tuning reduces representation bias but less effective for association bias.
Architectural and data quality improvements can mitigate negative impacts of data balancing.
Abstract
We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffirm prior conclusions that CLIP models can inadvertently absorb societal stereotypes. To counter this, we present a novel algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both representation and association biases (i.e. in first- and second-order statistics) in multimodal data. We use M4 to conduct an in-depth analysis taking into account various factors, such as the model, representation, and data size. Our study also explores the dynamic nature of how CLIP learns and unlearns biases. In particular, we find that fine-tuning is effective in countering representation biases, though its impact diminishes for association biases. Also, data balancing has a mixed impact on quality: it tends to…
Peer Reviews
Decision·ICLR 2024 poster
- The paper is well motivated and clearly written. - Simple data balancing strategies are proposed to tackle the bias issue, demonstrating promising results. - Comprehensive experimental results and analysis are presented, which may benefit the reader in relevant fields.
- While AB is relatively easy to mitigate, RB seems much more difficult to remove. In this regard, I would suggest the authors to shed more light on possible reasons and solutions. For example, I assume data augmentation shall be a promising workaround, and encourage the authors to explore more.
1. The empirical evidence of RB and AB is well supported in the experiments. 2. The proposed data balancing algorithm is principled with theoretical analysis. 3. The paper studies an interesting and important problem which may have a wide impact in real-world industrial applications, such as recommender systems and advertising.
1. The number of sensitive attributes in the experiments is limited to only gender and occupation. 2. Further experiments on proposed data balancing algorithm is lacking in the main text.
1. Nice definition of various types of bias in data or model, this is a solid foundation to understand the problem 2. Given a nice definition of bias such as gender, this work shows the effect of model training with various type settings. (More or less data, fine tuning with various length of training time) 3. Tested on various datasets and backbone designs. 4. The proposed balancing algorithm does seem to successfully diminish bias without compromising the quality of the model.
1. I hope to see figures that are easier to interpret. - For Figure 2 (top): It seems you intend to demonstrate that even with extra training data, bias persists. I struggled to determine which bars were being compared. A similar issue occurs with Figure 3. - Regarding Figure 4: At first glance, without referring to the captions, all the color bars appear identical. My initial interpretation was that the results were largely uniform across the board. 2. I find myself somewhat perplexed by the
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Taxonomy
TopicsWikis in Education and Collaboration · Educational Tools and Methods · Digital Storytelling and Education
MethodsContrastive Language-Image Pre-training