71 citations · 160 across the 11 of their papers we have counts for
11 papers
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
Yiqun Zhang, Yiu-ming Cheung
The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing cl…
Feature-Balanced Loss for Long-Tailed Visual Recognition
Mengke Li, Yiu-ming Cheung, Juyong Jiang
Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a bia…
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Mengke Li, Yiu-ming Cheung, Yang Lu +3
It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tai…
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
Yan Jin, Mengke Li, Yang Lu +2
Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heav…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Uniform tensor clustering by jointly exploring sample affinities of various orders
Hongmin Cai, Fei Qi, Junyu Li +4
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, result…
Label-Noise Learning with Intrinsically Long-Tailed Data
Yang Lu, Yiliang Zhang, Bo Han +2
Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth class…
Compact Neural Networks via Stacking Designed Basic Units
Weichao Lan, Yiu-ming Cheung, Juyong Jiang
Unstructured pruning has the limitation of dealing with the sparse and irregular weights. By contrast, structured pruning can help eliminate this drawback but it requires complex c…
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Xinyi Shang, Yang Lu, Yiu-ming Cheung +1
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID dat…
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data
Yiu-ming Cheung, Juyong Jiang, Feng Yu +1
Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horiz…
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
Xin Liu, Xingzhi Wang, Yiu-ming Cheung
Cross-modal hashing, favored for its effectiveness and efficiency, has received wide attention to facilitating efficient retrieval across different modalities. Nevertheless, most e…