22 citations · 107 across the 16 of their papers we have counts for
24 papers
Jacobian Norm for Unsupervised Source-Free Domain Adaptation
Weikai Li, Meng Cao, Songcan Chen
Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain. In such a scenario,…
A Similarity-based Framework for Classification Task
Zhongchen Ma, Songcan Chen
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting i…
Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task
Quan Feng, Songcan Chen
Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly focus on the scenario where la…
Rectified Euler k-means and Beyond
Yunxia Lin, Songcan chen
Euler k-means (EulerK) first maps data onto the unit hyper-sphere surface of equi-dimensional space via a complex mapping which induces the robust Euler kernel and next employs the…
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
Kun-Peng Ning, Lue Tao, Songcan Chen +1
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improvi…
Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection
Weikai Li, Chuanxing Geng, Songcan Chen
As the main workhorse for model selection, Cross Validation (CV) has achieved an empirical success due to its simplicity and intuitiveness. However, despite its ubiquitous role, CV…