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Found 7 papers, 7 papers with code
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns.
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
Ranked #6 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning
However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$.
OpenAUC: Towards AUC-Oriented Open-Set Recognition
In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction.
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.
Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions
As the core of the framework, the iterative relabeling module exploits the self-training principle to dynamically generate pseudo labels for user preferences.
