5 citations · 9 across the 24 of their papers we have counts for
6 papers · 1 filter
MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models
Weilun Feng, Haotong Qin, Chuanguang Yang +7
Diffusion models have received wide attention in generation tasks. However, the expensive computation cost prevents the application of diffusion models in resource-constrained scen…
Continual Learning in the Frequency Domain
Ruiqi Liu, Boyu Diao, Libo Huang +3
Continual learning (CL) is designed to learn new tasks while preserving existing knowledge. Replaying samples from earlier tasks has proven to be an effective method to mitigate th…
Relational Diffusion Distillation for Efficient Image Generation
Weilun Feng, Chuanguang Yang, Zhulin An +4
Although the diffusion model has achieved remarkable performance in the field of image generation, its high inference delay hinders its wide application in edge devices with scarce…
Online Policy Distillation with Decision-Attention
Xinqiang Yu, Chuanguang Yang, Chengqing Yu +3
Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a s…
IOR: Inversed Objects Replay for Incremental Object Detection
Zijia An, Boyu Diao, Libo Huang +3
Existing Incremental Object Detection (IOD) methods partially alleviate catastrophic forgetting when incrementally detecting new objects in real-world scenarios. However, many of t…
Exemplar-Free Class Incremental Learning via Incremental Representation
Libo Huang, Zhulin An, Yan Zeng +3
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any…