5 papers
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation
Yuling Jiao, Lican Kang, Jerry Zhijian Yang +2
Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain. W…
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
Feijiang Li, Zhenxiong Li, Jieting Wang +3
Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering…
Beyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
Jieting Wang, Huimei Shi, Feijiang Li +1
Time series forecasting is an important task that involves analyzing temporal dependencies and underlying patterns (such as trends, cyclicality, and seasonality) in historical data…
RI-Loss: A Learnable Residual-Informed Loss for Time Series Forecasting
Jieting Wang, Xiaolei Shang, Feijiang Li +1
Time series forecasting relies on predicting future values from historical data, yet most state-of-the-art approaches-including transformer and multilayer perceptron-based models-o…
Deep Embedding Clustering Driven by Sample Stability
Zhanwen Cheng, Feijiang Li, Jieting Wang +1
Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms hav…