19 papers with code ·
Methodology

No evaluation results yet. Help compare methods by
submit
evaluation metrics.

ICLR 2019 • Eric-mingjie/rethinking-network-pruning •

Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm.

ICLR 2019 • google-research/lottery-ticket-hypothesis •

Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.

Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.

In this paper, we address the problem of fast depth estimation on embedded systems.

CVPR 2018 • arunmallya/packnet •

This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting.

CVPR 2019 • NVlabs/Taylor_pruning •

On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0. 02% in the top-1 accuracy on ImageNet.

ICLR 2018 • xingyul/Sparse-Winograd-CNN •

First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations.

Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network.

alecwangcq/EigenDamage-Pytorch •

•Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices.

We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network.