5 papers
MAN++: Scaling Momentum Auxiliary Network for Supervised Local Learning in Vision Tasks
Junhao Su, Feiyu Zhu, Hengyu Shi +5
Deep learning typically relies on end-to-end backpropagation for training, a method that inherently suffers from issues such as update locking during parameter optimization, high G…
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank
Feiyu Zhu, Yuming Zhang, Xiuyuan Guo +4
Local learning offers an alternative to traditional end-to-end back-propagation in deep neural networks, significantly reducing GPU memory consumption. Although it has shown promis…
MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network
Yuming Zhang, Shouxin Zhang, Peizhe Wang +5
Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and diffi…
Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning
Xiuyuan Guo, Chengqi Xu, Guinan Guo +6
Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and s…
SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation
Changpeng Cai, Guinan Guo, Jiao Li +7
Most earlier researches on talking face generation have focused on the synchronization of lip motion and speech content. However, head pose and facial emotions are equally importan…