154 citations · 509 across the 50 of their papers we have counts for
14 papers · 1 filter
On the Role of Label Noise in the Feature Learning Process
Andi Han, Wei Huang, Zhanpeng Zhou +5
Deep learning with noisy labels presents significant challenges. In this work, we theoretically characterize the role of label noise from a feature learning perspective. Specifical…
Instance-dependent Label-noise Learning under a Structural Causal Model
Yu Yao, Tongliang Liu, Mingming Gong +3
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, r…
Learning from Similarity-Confidence Data
Yuzhou Cao, Lei Feng, Yitian Xu +3
Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a no…
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang, Gang Niu, Masashi Sugiyama
Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data,…
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
Wenkai Xu, Gang Niu, Aapo Hyvärinen +1
Summarizing large-scaled directed graphs into small-scale representations is a useful but less studied problem setting. Conventional clustering approaches, which based on "Min-Cut"…
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Krishna Menon +1
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped w…