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Found 4 papers, 1 papers with code
Layer-adaptive Structured Pruning Guided by Latency
Structured pruning can simplify network architecture and improve inference speed.
Boosting Pruned Networks with Linear Over-parameterization
To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning.
Conditional GANs with Auxiliary Discriminative Classifier
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on
Conditional Image Generation
on Tiny ImageNet
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning
This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance.
