Summary of Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models, By Donggeun Ko et al.
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models
by Donggeun Ko, Dongjun Lee, Namjun Park, Wonkyeong Shim, Jaekwang Kim
First submitted to arxiv on: 25 Nov 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 This paper introduces a novel pipeline called DiffuBias for enhancing classifier robustness by generating bias-conflict samples without requiring training during the generation phase. The method utilizes pre-trained diffusion and image captioning models to create images that challenge biases in classifiers, improving generalization capabilities. Unlike previous methods, DiffuBias does not require attribute labels or Generative Adversarial Networks (GANs) for debiasing. Instead, it leverages a stable diffusion model to generate bias-conflict samples, achieving state-of-the-art performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a problem with neural networks and images. When these networks are trained, they can learn biases that make them not work well in real-life situations. To solve this issue, the authors developed a new way to create fake images that challenge these biases. This approach uses special models that were already trained on other tasks. By creating these challenging images, the system can improve how well it works in different situations. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generalization » Image captioning
