105 citations · 225 across the 31 of their papers we have counts for
9 papers · 1 filter
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
Ren Wang, Haoliang Sun, Qi Wei +3
Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new ta…
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Guoqiang Wu, Chongxuan Li, Yilong Yin
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solv…
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning
Ziqi Zhou, Li Lian, Yilong Yin +1
Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open pr…
Active Source Free Domain Adaptation
Fan Wang, Zhongyi Han, Zhiyan Zhang +1
Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an ef…
Exploring Linear Feature Disentanglement For Neural Networks
Tiantian He, Zhibin Li, Yongshun Gong +3
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the o…
Learning to Rectify for Robust Learning with Noisy Labels
Haoliang Sun, Chenhui Guo, Qi Wei +2
Label noise significantly degrades the generalization ability of deep models in applications. Effective strategies and approaches, \textit{e.g.} re-weighting, or loss correction, a…
Tri-Branch Convolutional Neural Networks for Top- Focused Academic Performance Prediction
Chaoran Cui, Jian Zong, Yuling Ma +4
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, su…
MetaKernel: Learning Variational Random Features with Limited Labels
Yingjun Du, Haoliang Sun, Xiantong Zhen +4
Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-sho…
Towards Accurate and Robust Domain Adaptation under Noisy Environments
Zhongyi Han, Xian-Jin Gui, Chaoran Cui +1
In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation work…