55 citations · 263 across the 47 of their papers we have counts for
16 papers · 1 filter
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
Kaican Li, Weiyan Xie, Yongxiang Huang +5
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. H…
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
Kaican Li, Yifan Zhang, Lanqing Hong +2
Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks emplo…
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
Rui Sun, Fengwei Zhou, Zhenhua Dong +6
In this work, we propose {\it Fair-CDA}, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the feature…
Continual Learning by Modeling Intra-Class Variation
Longhui Yu, Tianyang Hu, Lanqing Hong +3
It has been observed that neural networks perform poorly when the data or tasks are presented sequentially. Unlike humans, neural networks suffer greatly from catastrophic forgetti…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…
Memory Replay with Data Compression for Continual Learning
Liyuan Wang, Xingxing Zhang, Kuo Yang +7
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves…
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
Haoyue Bai, Fengwei Zhou, Lanqing Hong +3
Recent advances on Out-of-Distribution (OoD) generalization reveal the robustness of deep learning models against distribution shifts. However, existing works focus on OoD algorith…
Adversarial Robustness for Unsupervised Domain Adaptation
Muhammad Awais, Fengwei Zhou, Hang Xu +4
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled…
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Kaichen Zhou, Lanqing Hong, Shoukang Hu +4
Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture…
Contextualizing Meta-Learning via Learning to Decompose
Han-Jia Ye, Da-Wei Zhou, Lanqing Hong +3
Meta-learning has emerged as an efficient approach for constructing target models based on support sets. For example, the meta-learned embeddings enable the construction of target…
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
Nanyang Ye, Kaican Li, Haoyue Bai +5
Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically…
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu +6
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a lear…
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Liyuan Wang, Kuo Yang, Chongxuan Li +3
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual lear…
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Fengwei Zhou, Jiawei Li, Chuanlong Xie +4
Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample…
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
Fan Bao, Kun Xu, Chongxuan Li +3
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition…