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Neural Network Compression

23 papers with code · Methodology
Subtask of Model Compression

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Neural Network Compression Framework for fast model inference

20 Feb 2020

In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).

NEURAL NETWORK COMPRESSION QUANTIZATION

The continuous categorical: a novel simplex-valued exponential family

20 Feb 2020

Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks.

NEURAL NETWORK COMPRESSION TRANSFER LEARNING

Neural Epitome Search for Architecture-Agnostic Network Compression

ICLR 2020

In this work, we propose a new perspective on network compression, i. e., network parameters can be disentangled from the architectures.

AUTOML NETWORK PRUNING NEURAL NETWORK COMPRESSION QUANTIZATION

Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

20 Dec 2019

The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures.

NEURAL NETWORK COMPRESSION

TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis

18 Dec 2019

Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices.

ACTIVE LEARNING NEURAL NETWORK COMPRESSION

Lossless Compression for 3DCNNs Based on Tensor Train Decomposition

8 Dec 2019

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks of video or 3D point cloud recognition.

NEURAL NETWORK COMPRESSION

Distilling Pixel-Wise Feature Similarities for Semantic Segmentation

31 Oct 2019

Directly adapting these methods to the task of semantic segmentation only brings marginal improvements.

NEURAL NETWORK COMPRESSION SEMANTIC SEGMENTATION

A Comparative Study of Neural Network Compression

24 Oct 2019

There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings.

NEURAL NETWORK COMPRESSION

Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach

14 Oct 2019

A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.

NEURAL NETWORK COMPRESSION QUANTIZATION

Similarity-Preserving Knowledge Distillation

ICCV 2019

Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network.

NEURAL NETWORK COMPRESSION