Supervised Representation Hash Codes Learning. - Huei-Fang Yang; Cheng-Hao Tu 001; Chu-Song Chen (ICS, 2018)

Equivalent Scanning Network of Unpadded CNNs. - Huei-Fang Yang; Ting-Yen Chen; Cheng-Hao Tu 001; Chu-Song Chen (IEEE Signal Process. Lett., 2018)

Adaptive Labeling for Deep Learning to Hash. - Huei-Fang Yang; Cheng-Hao Tu 001; Chu-Song Chen (CVPR Workshops, 2019)

Adaptive Labeling For Hash Code Learning Via Neural Networks. - Huei-Fang Yang; Cheng-Hao Tu 001; Chu-Song Chen (ICIP, 2019)

Compacting, Picking and Growing for Unforgetting Continual Learning. - Steven C. Y. Hung; Cheng-Hao Tu 001; Cheng-En Wu; Chien-Hung Chen; Yi-Ming Chan; Chu-Song Chen (NeurIPS, 2019)

Extending Conditional Convolution Structures For Enhancing Multitasking Continual Learning. - Cheng-Hao Tu 001; Cheng-En Wu; Chu-Song Chen (APSIPA, 2020)

Pruning Depthwise Separable Convolutions for MobileNet Compression. - Cheng-Hao Tu 001; Jia-Hong Lee; Yi-Ming Chan; Chu-Song Chen (IJCNN, 2020)

Defect Detection Using Deep Lifelong Learning. - Chien-Hung Chen; Cheng-Hao Tu 001; Jia-Da Li; Chu-Song Chen (INDIN, 2021)

SemanticHash: Hash Coding Via Semantics-Guided Label Prototype Learning. - Cheng-Hao Tu 001; Huei-Fang Yang; Shih-Min Yang; Mei-Chen Yeh; Chu-Song Chen (IEEE Trans. Artif. Intell., 2021)

On Pre-Training for Federated Learning. - Hong-You Chen; Cheng-Hao Tu 001; Ziwei Li; Han-Wei Shen; Wei-Lun Chao (CoRR, 2022)

Learning Binary Hash Codes Based on Adaptable Label Representations. - Huei-Fang Yang; Cheng-Hao Tu 001; Chu-Song Chen (IEEE Trans. Neural Networks Learn. Syst., 2022)

Learning Fractals by Gradient Descent. - Cheng-Hao Tu 001; Hong-You Chen; David Carlyn; Wei-Lun Chao (AAAI, 2023)

Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning. - Cheng-Hao Tu 001; Zheda Mai; Wei-Lun Chao (CVPR, 2023)

On the Importance and Applicability of Pre-Training for Federated Learning. - Hong-You Chen; Cheng-Hao Tu 001; Ziwei Li; Han-Wei Shen; Wei-Lun Chao (ICLR, 2023)

Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data. - Cheng-Hao Tu 001; Hong-You Chen; Zheda Mai; Jike Zhong; Vardaan Pahuja; Tanya Y. Berger-Wolf; Song Gao 001; Charles V. Stewart; Yu Su 001; Wei-Lun Chao (NeurIPS, 2023)

Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs. - Vardaan Pahuja; Weidi Luo; Yu Gu 016; Cheng-Hao Tu 001; Hong-You Chen; Tanya Y. Berger-Wolf; Charles V. Stewart; Song Gao 001; Wei-Lun Chao; Yu Su 001 (CIKM, 2024)

Fine-Tuning is Fine, if Calibrated. - Zheda Mai; Arpita Chowdhury; Ping Zhang 016; Cheng-Hao Tu 001; Hong-You Chen; Vardaan Pahuja; Tanya Y. Berger-Wolf; Song Gao 001; Charles V. Stewart; Yu Su 001; Wei-Lun Chao (NeurIPS, 2024)

Bringing Back the Context: Camera Trap Species Identification as Link Prediction on Multimodal Knowledge Graphs. - Vardaan Pahuja; Weidi Luo; Yu Gu 016; Cheng-Hao Tu 001; Hong-You Chen; Tanya Y. Berger-Wolf; Charles V. Stewart; Song Gao 001; Wei-Lun Chao; Yu Su 001 (CoRR, 2024)

Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Transfer Learning (PETL) in Visual Recognition. - Zheda Mai; Ping Zhang 016; Cheng-Hao Tu 001; Hong-You Chen; Li Zhang 003; Wei-Lun Chao (CoRR, 2024)

Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition. - Zheda Mai; Ping Zhang 016; Cheng-Hao Tu 001; Hong-You Chen; Quang-Huy Nguyen; Li Zhang 003; Wei-Lun Chao (CVPR, 2025)