4 papers · 1 filter
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…
Momentum Auxiliary Network for Supervised Local Learning
Junhao Su, Changpeng Cai, Feiyu Zhu +4
Deep neural networks conventionally employ end-to-end backpropagation for their training process, which lacks biological credibility and triggers a locking dilemma during network p…