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Found 3 papers, 3 papers with code
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
In this work, we propose to use out-of-distribution samples, i. e., unlabeled samples coming from outside the target classes, to improve few-shot learning.
Network Pruning That Matters: A Case Study on Retraining Variants
Our results emphasize the cruciality of the learning rate schedule in pruned network retraining - a detail often overlooked by practitioners during the implementation of network pruning.
Ranked #10 on
Network Pruning
on ImageNet
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation
In this work, we show that strong ensembles can be constructed from snapshots of iterative pruning, which achieve competitive performance and vary in network structure.
