Salient ImageNet: How to discover spurious features in Deep Learning?
This addresses the unreliability of deep learning models in real-world applications by providing a scalable method to identify spurious features, which is crucial for improving model robustness and evaluation, though it is incremental in automating feature discovery.
The paper tackles the problem of deep neural networks relying on spurious features for predictions in image classification, introducing a general framework to discover and localize such features with minimal human supervision, resulting in the Salient Imagenet dataset that reveals models heavily use spurious features, indicating standard accuracy is insufficient for assessment.
Deep neural networks can be unreliable in the real world especially when they heavily use {\it spurious} features for their predictions. Focusing on image classifications, we define {\it core features} as the set of visual features that are always a part of the object definition while {\it spurious features} are the ones that are likely to {\it co-occur} with the object but not a part of it (e.g., attribute "fingers" for class "band aid"). Traditional methods for discovering spurious features either require extensive human annotations (thus, not scalable), or are useful on specific models. In this work, we introduce a {\it general} framework to discover a subset of spurious and core visual features used in inferences of a general model and localize them on a large number of images with minimal human supervision. Our methodology is based on this key idea: to identify spurious or core \textit{visual features} used in model predictions, we identify spurious or core \textit{neural features} (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature). We then show that these neural feature annotations {\it generalize} extremely well to many more images {\it without} any human supervision. We use the activation maps for these neural features as the soft masks to highlight spurious or core visual features. Using this methodology, we introduce the {\it Salient Imagenet} dataset containing core and spurious masks for a large set of samples from Imagenet. Using this dataset, we show that several popular Imagenet models rely heavily on various spurious features in their predictions, indicating the standard accuracy alone is not sufficient to fully assess model performance. Code and dataset for reproducing all experiments in the paper is available at \url{https://github.com/singlasahil14/salient_imagenet}.