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Found 3 papers, 3 papers with code
Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale
Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data.
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data.
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