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

Mohammad Azizmalayeri; Reza Abbasi; Amir Hosein Haji Mohammad rezaie; Reihaneh Zohrabi; Mahdi Amiri; Mohammad Taghi Manzuri; Mohammad Hossein Rohban

arXiv · 2311.00079

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

Mohammad Azizmalayeri·Reza Abbasi·Amir Hosein Haji Mohammad rezaie·Reihaneh Zohrabi·Mahdi Amiri·Mohammad Taghi Manzuri·Mohammad Hossein Rohban

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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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BibTeX RIS

Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie, Reihaneh Zohrabi, Mahdi Amiri, Mohammad Taghi Manzuri, Mohammad Hossein Rohban. 2023-10-31. Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection. https://arxiv.org/abs/2311.00079

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