Search Results for author:
Found 2 papers, 1 papers with code
LIT: Learned Intermediate Representation Training for Model Compression
In this work, we introduce Learned Intermediate representation Training (LIT), a novel model compression technique that outperforms a range of recent model compression techniques by leveraging the highly repetitive structure of modern DNNs (e. g., ResNet).
LIT: Block-wise Intermediate Representation Training for Model Compression
Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
