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Found 4 papers, 2 papers with code
SLiMe: Segment Like Me
Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image.
TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier.
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy.
