Low-rank compression
8 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Low-rank compression models and implementationsMost implemented papers
Domain-adaptive deep network compression
We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
lucaslie/torchprune
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NeurIPS 2021
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall compression.
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
ygcats/Scalable-Low-Rank-Compression-for-Neural-Networks
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Compressing DNNs is important for the real-world applications operating on resource-constrained devices.
A flexible, extensible software framework for model compression based on the LC algorithm
UCMerced-ML/LC-model-compression
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We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer
UCMerced-ML/LC-model-compression
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CVPR 2020
Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix.
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
UCMerced-ML/LC-model-compression
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However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
Compact Model Training by Low-Rank Projection with Energy Transfer
bzqlin/lrpet
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In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
TT-NF: Tensor Train Neural Fields
toshas/ttnf
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Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations.
