Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Keywords: Spurious correlation, Last-layer retraining, Open vocabulary object detection, Spurious features
TL;DR: Our paper proposes a novel ranking framework that leverages an object detection technique to identify and sort images without spurious cues for effective last-layer retraining of deep neural networks.
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.
Submission Number: 28
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