REVIEW 4 cited by
Does enhanced shape bias improve neural network robustness to common corruptions?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen test data from the same distribution as the training data but often fail to generalize to out-of-distribution data. It has been shown that augmenting the training data with different image styles decreases this texture bias in favor of increased shape bias while at the same time improving robustness to common corruptions, such as noise and blur. Commonly, this is interpreted as shape bias increasing corruption robustness. However, this relationship is only hypothesized. We perform a systematic study of different ways of composing inputs based on natural images, explicit edge information, and stylization. While stylization is essential for achieving high corruption robustness, we do not find a clear correlation between shape bias and robustness. We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct.
Forward citations
Cited by 4 Pith papers
-
Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
S&D improves corruption robustness by selecting the most stable internal pathways under a synthetic corruption and diversifying them through symmetric weight tweaks, with no test-time overhead.
-
On the Reliability of Cue Conflict and Beyond
Stylized cue-conflict bias scores are confounded by impure cues, imbalance, ratio metrics and restricted labels; REFINED-BIAS supplies pure balanced stimuli and full-label MRR sensitivity for reliable diagnosis.
-
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities
By probing visual, projection, and response representations, the authors find that most VLM visual knowledge loss for recognition and counting occurs in the language decoder, while spatial understanding is lost in the...
-
The Art of Deception: Color Visual Illusions and Diffusion Models
DDIM inversion in diffusion models produces brightness and color shifts that track human visual illusions, and a diffusion-based optimizer can generate new illusions in realistic images that fool human observers.
Discussion (0). Continue with ORCID to comment.