3 citations · 6 across the 7 of their papers we have counts for
7 papers
PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings
Sinchee Chin, Yinuo Ma, Xiaochen Yang +2
Unsupervised pathology detection trains models on non-pathological data to flag deviations as pathologies, offering strong generalizability for identifying novel diseases and avoid…
VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection
Sinchee Chin, Fan Zhang, Xiaochen Yang +5
Time Series Anomaly Detection (TSAD) is essential for uncovering rare and potentially harmful events in unlabeled time series data. Existing methods are highly dependent on clean,…
PUAL: A Classifier on Trifurcate Positive-Unlabeled Data
Xiaoke Wang, Xiaochen Yang, Rui Zhu +1
Positive-unlabeled (PU) learning aims to train a classifier using the data containing only labeled-positive instances and unlabeled instances. However, existing PU learning methods…
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Xiaoxu Li, Liyun Yu, Xiaochen Yang +4
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the samp…
Towards Certified Robustness of Distance Metric Learning
Xiaochen Yang, Yiwen Guo, Mingzhi Dong +1
Metric learning aims to learn a distance metric such that semantically similar instances are pulled together while dissimilar instances are pushed away. Many existing methods consi…
Metric Learning via Maximizing the Lipschitz Margin Ratio
Mingzhi Dong, Xiaochen Yang, Yang Wu +1
In this paper, we propose the Lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio. This framework enables the integration of…