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Found 2 papers, 2 papers with code
Feature Separation and Recalibration for Adversarial Robustness
The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack.
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features.
Ranked #3 on
Unsupervised Vehicle Re-Identification
on VeRi-776
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