FairerCLIP: Debiasing CLIP's Zero-Shot Predictions Using RKHS Functions

Alapfogalmak

FairerCLIP proposes a general approach to make zero-shot predictions of CLIP more fair and robust by debiasing image and text representations using reproducing kernel Hilbert spaces (RKHS).

Kivonat

This paper introduces FairerCLIP, a method for debiasing CLIP's zero-shot predictions. It addresses biases in vision-language models by focusing on spurious correlations and intrinsic dependencies. The paper outlines the problem setup, choice of dependence measures, objective functions, and the solution process. Experimental evaluations on various datasets demonstrate the effectiveness of FairerCLIP in mitigating bias while maintaining high accuracy. Additionally, ablation studies show the importance of different components in improving performance.

Introduction: Discusses biases in vision-language models like CLIP.
Problem Setup: Defines the joint random variables and aims to debias image and text features.
Choice of Dependence Measure: Adopts a statistical dependence measure based on RKHS.
Objective Function: Formulates an optimization problem to mitigate bias in CLIP's predictions.
Solution Process: Describes an alternating optimization approach for training FairerCLIP.
Experimental Evaluation: Evaluates FairerCLIP on various datasets to showcase its effectiveness.
Ablation Studies: Investigates the impact of different components on FairerCLIP's performance.

Statisztikák

"FairerCLIP achieves appreciable accuracy gains on benchmark fairness and spurious correlation datasets."
"FairerCLIP significantly outperforms baselines under sample-limited conditions."
"FairerCLIP is 4×-10× faster in training than existing methods."

Idézetek

"We propose FairerCLIP to address the aforementioned limitations of existing debiasing approaches."
"Empirically, FairerCLIP achieves appreciable accuracy gains on benchmark fairness and spurious correlation datasets over their respective baselines."
"An overview of FairerCLIP in its train and inference phases along with how we integrate this transformation over the underlying CLIP model is shown."

Főbb Kivonatok

FairerCLIP

by Sepehr Dehda... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15593.pdf

Mélyebb kérdések

How can FairerCLIP be adapted for other types of biases beyond demographic attributes

FairerCLIP can be adapted for other types of biases beyond demographic attributes by adjusting the sensitive attribute and target attribute in the training process. Instead of focusing solely on demographic attributes like sex or race, FairerCLIP can be tailored to address biases related to various other factors such as image background, lighting conditions, object orientation, or any other characteristic that may introduce bias in the model's predictions. By modifying the target and sensitive attributes accordingly, FairerCLIP can effectively debias representations across a wide range of potential biases present in the data.

What are potential drawbacks or limitations of using RKHS for debiasing compared to other methods

While RKHS offers several advantages for debiasing models like FairerCLIP, there are also potential drawbacks and limitations compared to other methods. One limitation is computational complexity - using RKHS may require significant computational resources due to kernel matrix computations and memory requirements for large datasets. Additionally, tuning hyperparameters for kernel functions in RKHS can be challenging and time-consuming compared to simpler linear models. Another drawback is interpretability - the non-linearity introduced by kernel functions in RKHS may make it harder to interpret how features are being transformed during debiasing compared to more straightforward linear methods.

How might incorporating additional modalities into the training process impact the effectiveness of FairerCLIP

Incorporating additional modalities into the training process could potentially enhance the effectiveness of FairerCLIP by providing a richer set of information for learning representations. For example, including audio or video modalities along with text and images could lead to more comprehensive embeddings that capture diverse aspects of data characteristics. This multi-modal approach could help improve generalization capabilities and robustness against different types of biases present across multiple modalities. However, integrating additional modalities would also increase complexity in terms of feature extraction, alignment between modalities, and overall model architecture design which might require more sophisticated techniques for effective integration while maintaining efficiency and performance levels achieved by FairerCLIP with single modality inputs.