Shypd.aiThe largest open index of AI toolsEfficient-Deep-Learning is an open-source collection of methods for deep neural network compression and acceleration. It provides resources for optimizing deep learning models through techniques like pruning and quantization.
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Efficient-Deep-Learning is a comprehensive GitHub repository dedicated to collecting recent methods for deep neural network compression and acceleration. It categorizes techniques into neural architecture re-design or search (NAS), pruning (including structured and unstructured), quantization, matrix/low-rank decomposition, and knowledge distillation (KD). The repository particularly focuses on pruning (with lottery ticket hypothesis or LTH as a sub-topic), KD, and quantization, offering a curated list of relevant papers and surveys dating back to the 1980s. It serves as a valuable resource for researchers and practitioners aiming to improve the efficiency, speed, and compactness of deep learning models.
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