most citedTowards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation

5 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2025

Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents

Xiao Wang, Ke Qin, Dongyang Zhang +2

Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure ite…

cs.CV2025

Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation

Muquan Li, Hang Gou, Dongyang Zhang +4

The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. Howev…

cs.CV2024

Adaptive Dataset Quantization

Muquan Li, Dongyang Zhang, Qiang Dong +2

Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data st…

cs.CV2024★ 5 cited

Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation

Muquan Li, Dongyang Zhang, Tao He +3

Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data.…

cs.CV2024

Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

Tao He, Tongtong Wu, Dongyang Zhang +3

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning proc…