Summary of Improving Bias in Facial Attribute Classification: a Combined Impact Of Kl Divergence Induced Loss Function and Dual Attention, by Shweta Patel and Dakshina Ranjan Kisku
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention
by Shweta Patel, Dakshina Ranjan Kisku
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed method utilizes a dual attention mechanism with a pre-trained Inception-ResNet V1 model, incorporating KL-divergence regularization and cross-entropy loss function. This approach aims to reduce bias while improving accuracy and computational efficiency through transfer learning. By enhancing fairness and classification accuracy, this paper contributes to addressing the challenges of demographic bias in facial recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to create a fair facial recognition system that works equally well for all demographics. They want to ensure that gender and racial classifications are accurate and unbiased. To achieve this, they’re introducing new techniques to mitigate biases. However, there are still many challenges, such as ensuring data is diverse, balancing fairness with accuracy, and measuring bias. |
Keywords
» Artificial intelligence » Attention » Classification » Cross entropy » Loss function » Regularization » Resnet » Transfer learning
