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medi

[ICLR 2022] "Meta Discovery: Learning to Discover Novel Classes given Very Limited Data"

memorization-discrepancy

[ICML 2023] "Exploring Model Dynamics for Accumulative Poisoning Discovery"

mm-attack

[ICML 2022] "Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack"

mmd-mp

[ICLR 2024] "Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy"

neglabel

[ICLR 2024 Spotlight] "Negative Label Guided OOD Detection with Pretrained Vision-Language Models"

pair

[ICLR 2023] "PAIR: Towards Mitigating the Optimization Dilemma in OOD Generalization"

rgib

[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"

robot

[ICLR 2023] "A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond"

sammd

[ICML 2021] "Maximum Mean Discrepancy Test is Aware of Adversarial Attacks"

sccd

[TKDD 2023] "Server-Client Collaborative Distillation for Federated Reinforcement Learning"

sfat

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

sfat-mindspore

SFAT implemented with MindSpore

sigua

[ICML 2020] "SIGUA: Forgetting May Make Learning with Noisy Labels More Robust"

soda

[NeurIPS 2023] "SODA: Robust Training of Test-Time Data Adaptors"

tmlr-group.github.io

tohan

[NeurIPS 2021] "TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation"

twin-sight

[ICLR 2024] "Robust Training of Federated Models with Extremely Label Deficiency"

unleashing-mask

[ICML 2023] "Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability"

vhl

[ICML 2022] "Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning"

watermarking

[NeurIPS 2022] "Watermarking for Out-of-distribution Detection"