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Found 3 papers, 1 papers with code
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment
We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent.
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold
Thus, they neglect the critical characteristic of GANs: their local density structure over their learned manifold.
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps
To fill in this gap, we propose to address the channel pruning problem from a novel perspective by leveraging the interpretations of a model to steer the pruning process, thereby utilizing information from both inputs and outputs of the model.
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