From b28f6c5d9f301472f5617fb1a9c0a044a747604c Mon Sep 17 00:00:00 2001
From: Trance-0 <60459821+Trance-0@users.noreply.github.com>
Date: Tue, 4 Nov 2025 14:04:39 -0600
Subject: [PATCH] Update CSE5519_H4.md

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 # CSE5519 Advances in Computer Vision (Topic H: 2024: Safety, Robustness, and Evaluation of CV Models)
 
+## Efficient Bias Mitigation Without Privileged Information
+
+[link to the paper](https://arxiv.org/pdf/2409.17691)
+
+TAB: Targeted Augmentation for Bias mitigation
+
+1. Loss history embedding construction (use Helper model to generate loss history for training dataset)
+2. Loss aware partitioning (partition the training dataset into groups based on the loss history, reweight the loss of each group to balance the dataset)
+3. Group-balanced dataset generation (generate a new dataset by sampling from the groups based on the reweighting)
+4. Robust model training (train the model on the new dataset)
+
+> [!TIP]
+>
+> This paper is a good example of how to mitigate bias in a dataset without using privileged information.
+>
+> However, the mitigation is heavy relied on the loss history, which might be different for each model architecture. Thus, the produced dataset may not be generalizable to other models.
+>
+> How to evaluate the bias mitigation effect across different models and different datasets?
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