A graph-attention RL agent with a binary channel-level action space and a self-competition reward prunes CNNs at fixed FLOPs budgets, giving competitive but not uniformly state-of-the-art accuracy.
Learning to Prune Filters in Convolutional Neural Networks
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abstract
Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumptions. This paper presents a learning algorithm to simplify and speed up these CNNs. Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way. With the help of a novel reward function, our agents removes a significant number of filters in CNNs while maintaining performance at a desired level. Moreover, this method provides an easy control of the tradeoff between network performance and its scale. Per- formance of our algorithm is validated with comprehensive pruning experiments on several popular CNNs for visual recognition and semantic segmentation tasks.
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Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning
A graph-attention RL agent with a binary channel-level action space and a self-competition reward prunes CNNs at fixed FLOPs budgets, giving competitive but not uniformly state-of-the-art accuracy.