Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection

• URL: http://arxiv.org/abs/2311.00079v1
• Date: Tue, 31 Oct 2023 18:44:03 GMT
• Title: Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
• Authors: Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie, Reihaneh Zohrabi, Mahdi Amiri, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
• Abstract summary: We propose a novel ranking framework to identify images without spurious cues. We use the object detector as a measure to score the presence of the target object in the images. Next, the images are sorted based on this score, and the last-layer of the model is retrained on a subset of the data with the highest scores.
• Score: 5.199218657137718
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliability. A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues. Nevertheless, selecting this subset requires human supervision, which reduces its scalability. Moreover, spurious cues may still exist in the selected subset. As a solution to this problem, we propose a novel ranking framework that leverages an open vocabulary object detection technique to identify images without spurious cues. More specifically, we use the object detector as a measure to score the presence of the target object in the images. Next, the images are sorted based on this score, and the last-layer of the model is retrained on a subset of the data with the highest scores. Our experiments on the ImageNet-1k dataset demonstrate the effectiveness of this ranking framework in sorting images based on spuriousness and using them for last-layer retraining.

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