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Extensive experiments on three ZSL benchmarks demonstrate that ZeroDiff++ not only achieves significant improvements over existing ZSL methods but also maintains robust performance even with scarce training data, and mitigates data scarcity further.
Findings show that explanation stability is a distinct reliability axis for TTA in computational pathology, and releases the metric, protocol, and full benchmark to support future work on adaptation methods that are not only accurate, but also stable and clinically auditable.
CARE is introduced, a closed-form concept erasure operator that replaces the raw target direction with a kept-subspace-aware direction computed from a small bank of retained concept anchors, and preserves non-target concepts more faithfully while maintaining competitive erasure across instance, style, and celebrity con...
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