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Towards last-layer retraining for group robustness with fewer annotations
T LaBonte, V Muthukumar… - Advances in Neural …, 2023 - proceedings.neurips.cc
Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious
correlations and poor generalization on minority groups. The recent deep feature …
correlations and poor generalization on minority groups. The recent deep feature …
Efficient Bias Mitigation Without Privileged Information
M Espinosa Zarlenga, S Sankaranarayanan… - … on Computer Vision, 2024 - Springer
Deep neural networks trained via empirical risk minimization often exhibit significant
performance disparities across groups, particularly when group and task labels are …
performance disparities across groups, particularly when group and task labels are …
Do humans and machines have the same eyes? human-machine perceptual differences on image classification
Trained computer vision models are assumed to solve vision tasks by imitating human
behavior learned from training labels. Most efforts in recent vision research focus on …
behavior learned from training labels. Most efforts in recent vision research focus on …
Amend to alignment: decoupled prompt tuning for mitigating spurious correlation in vision-language models
Fine-tuning the learnable prompt for a pre-trained vision-language model (VLM), such as
CLIP, has demonstrated exceptional efficiency in adapting to a broad range of downstream …
CLIP, has demonstrated exceptional efficiency in adapting to a broad range of downstream …