6 papers
Replacement Learning: Training Neural Networks with Fewer Parameters
Yuming Zhang, Peizhe Wang, Tianyang Han +5
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since…
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…
Replacement Learning: Training Vision Tasks with Fewer Learnable Parameters
Yuming Zhang, Peizhe Wang, Shouxin Zhang +3
Traditional end-to-end deep learning models often enhance feature representation and overall performance by increasing the depth and complexity of the network during training. Howe…
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…
HPFF: Hierarchical Locally Supervised Learning with Patch Feature Fusion
Junhao Su, Chenghao He, Feiyu Zhu +3
Traditional deep learning relies on end-to-end backpropagation for training, but it suffers from drawbacks such as high memory consumption and not aligning with biological neural n…
GSO-YOLO: Global Stability Optimization YOLO for Construction Site Detection
Yuming Zhang, Dongzhi Guan, Shouxin Zhang +3
Safety issues at construction sites have long plagued the industry, posing risks to worker safety and causing economic damage due to potential hazards. With the advancement of arti…