https://github.com/code-implementation1/Code4/tree/main/lenet
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
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Network Pruning is a popular approach to reduce a heavy network to obtain a light-weight form by removing redundancy in the heavy network. In this approach, a complex over-parameterized network is first trained, then pruned based on come criterions, and finally fine-tuned to achieve comparable performance with reduced parameters.
Source: Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
A Simple and Effective Pruning Approach for Large Language Models
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
SNIP: Single-shot Network Pruning based on Connection Sensitivity
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
SNIP: Single-shot Network Pruning based on Connection Sensitivity
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
SCOP: Scientific Control for Reliable Neural Network Pruning
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
SNIP: Single-shot Network Pruning based on Connection Sensitivity
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
SNIP: Single-shot Network Pruning based on Connection Sensitivity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.