28 citations · 59 across the 13 of their papers we have counts for
14 papers
Denoising Implicit Feedback for Cold-start Recommendation
Gaode Chen, Shicheng Wang, Shikun Li +8
Implicit feedback is widely used in recommender systems due to its accessibility and generality, yet it usually presents noisy samples (e.g., clickbait, position bias). Meanwhile,…
Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
Bochao Liu, Shiming Ge, Pengju Wang +2
While many deep learning models trained on private datasets have been deployed in various practical tasks, they may pose a privacy leakage risk as attackers could recover informati…
TopoTTA: Topology-Enhanced Test-Time Adaptation for Tubular Structure Segmentation
Jiale Zhou, Wenhan Wang, Shikun Li +6
Tubular structure segmentation (TSS) is important for various applications, such as hemodynamic analysis and route navigation. Despite significant progress in TSS, domain shifts re…
Federated Learning with Label-Masking Distillation
Jianghu Lu, Shikun Li, Kexin Bao +3
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In…
Learning Natural Consistency Representation for Face Forgery Video Detection
Daichi Zhang, Zihao Xiao, Shikun Li +3
Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to sp…
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Hansong Zhang, Shikun Li, Fanzhao Lin +3
Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date,…
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Hansong Zhang, Shikun Li, Pengju Wang +2
Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation h…
Coupled Confusion Correction: Learning from Crowds with Sparse Annotations
Hansong Zhang, Shikun Li, Dan Zeng +2
As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sou…
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
Shikun Li, Xiaobo Xia, Hansong Zhang +2
Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative…
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
Shikun Li, Xiaobo Xia, Jiankang Deng +2
Learning from crowds describes that the annotations of training data are obtained with crowd-sourcing services. Multiple annotators each complete their own small part of the annota…
Model Conversion via Differentially Private Data-Free Distillation
Bochao Liu, Pengju Wang, Shikun Li +2
While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment whic…
Trustable Co-label Learning from Multiple Noisy Annotators
Shikun Li, Tongliang Liu, Jiyong Tan +2
Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multipl…
Selective-Supervised Contrastive Learning with Noisy Labels
Shikun Li, Xiaobo Xia, Shiming Ge +1
Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated l…
Student Network Learning via Evolutionary Knowledge Distillation
Kangkai Zhang, Chunhui Zhang, Shikun Li +2
Knowledge distillation provides an effective way to transfer knowledge via teacher-student learning, where most existing distillation approaches apply a fixed pre-trained model as…