4 citations · 4 across the 6 of their papers we have counts for
6 papers
Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates
Yixing Xu, Chao Li, Xuanwu Yin +4
Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large language models (LLMs) to specific dow…
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
Haowei Zhu, Dehua Tang, Ji Liu +12
Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resou…
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
Guanchen Li, Xiandong Zhao, Lian Liu +6
Pre-trained language models (PLMs) are engineered to be robust in contextual understanding and exhibit outstanding performance in various natural language processing tasks. However…
Towards Scale-Aware Full Surround Monodepth with Transformers
Yuchen Yang, Xinyi Wang, Dong Li +3
Full surround monodepth (FSM) methods can learn from multiple camera views simultaneously in a self-supervised manner to predict the scale-aware depth, which is more practical for…
Sparse Laneformer
Ji Liu, Zifeng Zhang, Mingjie Lu +7
Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, whic…
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Ji Liu, Dehua Tang, Yuanxian Huang +9
Traditional channel-wise pruning methods by reducing network channels struggle to effectively prune efficient CNN models with depth-wise convolutional layers and certain efficient…