70 citations · 341 across the 32 of their papers we have counts for
10 papers · 1 filter
Structured Cooperative Learning with Graphical Model Priors
Shuangtong Li, Tianyi Zhou, Xinmei Tian +1
We study how to train personalized models for different tasks on decentralized devices with limited local data. We propose "Structured Cooperative Learning (SCooL)", in which a coo…
Moderately Distributional Exploration for Domain Generalization
Rui Dai, Yonggang Zhang, Zhen Fang +2
Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches…
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
Li Shen, Yan Sun, Zhiyuan Yu +3
The field of deep learning has witnessed significant progress, particularly in computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models tr…
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Kaiwen Yang, Yanchao Sun, Jiahao Su +5
Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on auto…
Towards Lightweight Black-Box Attacks against Deep Neural Networks
Chenghao Sun, Yonggang Zhang, Wan Chaoqun +5
Black-box attacks can generate adversarial examples without accessing the parameters of target model, largely exacerbating the threats of deployed deep neural networks (DNNs). Howe…
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
Rong Dai, Li Shen, Fengxiang He +2
Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user. However, existing wo…
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
Zhen Huang, Xu Shen, Jun Xing +6
Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model or ensemble to a student model. Its success is generally attributed to the privileged…
CausalAdv: Adversarial Robustness through the Lens of Causality
Yonggang Zhang, Mingming Gong, Tongliang Liu +5
The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. As causal reasoning has an instinct for modelling distribution change…
On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning
Ya Li, Xinmei Tian, Tongliang Liu +1
Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always…
Domain Generalization via Conditional Invariant Representation
Ya Li, Mingming Gong, Xinmei Tian +2
Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data a…