41 citations · 89 across the 8 of their papers we have counts for
11 papers
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou +5
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…
Boosting Out-of-distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie +6
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais, Fengwei Zhou, Chuanlong Xie +3
Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is…
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Hang Xu, Ning Kang, Gengwei Zhang +3
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a…
Towards a Theoretical Framework of Out-of-Distribution Generalization
Haotian Ye, Chuanlong Xie, Tianle Cai +3
Generalization to out-of-distribution (OOD) data is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms that mainly…
Out-of-Distribution Generalization Analysis via Influence Function
Haotian Ye, Chuanlong Xie, Yue Liu +1
The mismatch between training and target data is one major challenge for current machine learning systems. When training data is collected from multiple domains and the target doma…