6 citations · 6 across the 4 of their papers we have counts for
4 papers
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios
Xinlei Huang, Jialiang Tang, Xubin Zheng +3
Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-l…
Benchmarking hybrid digitized-counterdiabatic quantum optimization
Ruoqian Xu, Jialiang Tang, Pranav Chandarana +4
Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of near-term quantum computers, utilizing parameterized quantum ci…
Direct Distillation between Different Domains
Jialiang Tang, Shuo Chen, Gang Niu +4
Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same…
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images
Jialiang Tang, Shuo Chen, Gang Niu +2
Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible w…