Spectral Tensor Train Parameterization of Deep Learning Layers

Overview

Spectral Tensor Train Parameterization of Deep Learning Layers

This repository is the official implementation of our AISTATS 2021 paper titled "Spectral Tensor Train Parameterization of Deep Learning Layers" by Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, and Luc Van Gool [arXiv] [PMLR].

It demonstrates how to perform low-rank neural network reparameterization and its stable training in a compressed form. The code provides all experiments (GAN and Image Classification) from the paper (see configs/aistats21 directory) with the following types of reparameterizations: SNGAN, SRGAN, SVDP, or STTP.

STTP teaser

Installation

All experiments can be reproduced on a single 11Gb GPU.

Clone the repository, then create a new virtual environment, and install python dependencies into it:

python3 -m venv venv_sttp
source venv_sttp/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt

In case of problems with generic requirements, fall back to requirements_reproducibility.txt.

Logging

The code performs logging to the console, tensorboard file in the experiment log directory, and also Weights and Biases (wandb). Upon the first run, please enter your wandb credentials, which can be obtained by registering a free account with the service.

Creating Environment Config

The training script allows specifying multiple yml config files, which will be concatenated during execution. This is done to separate experiment configs from environment configs. To start running experiments, create your own config file with a few environment settings, similar to configs/env_lsf.yml. Generally, you only need to update paths; see other fields explained in the config reference.

Training

Choose a preconfigured experiment from any of the configs/aistats21 directories, or compose your own config using the config reference, and run the following command:

CUDA_VISIBLE_DEVICES=0 python -m src.train --cfg configs/env_yours.yml --cfg configs/experiment.yml

Poster

STTP poster

Citation

Please cite our work if you found it useful:

@InProceedings{obukhov2021spectral,
  title={Spectral Tensor Train Parameterization of Deep Learning Layers},
  author={Obukhov, Anton and Rakhuba, Maxim and Liniger, Alexander and Huang, Zhiwu and Georgoulis, Stamatios and Dai, Dengxin and Van Gool, Luc},
  booktitle={Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages={3547--3555},
  year={2021},
  editor={Banerjee, Arindam and Fukumizu, Kenji},
  volume={130},
  series={Proceedings of Machine Learning Research},
  month={13--15 Apr},
  publisher={PMLR},
  pdf={http://proceedings.mlr.press/v130/obukhov21a/obukhov21a.pdf},
  url={http://proceedings.mlr.press/v130/obukhov21a.html}
}

License

This software is released under a CC-BY-NC 4.0 license, which allows personal and research use only. For a commercial license, please contact the authors. You can view a license summary here.

Portions of source code taken from external sources are annotated with links to original files and their corresponding licenses.

Acknowledgements

This work was supported by Toyota Motor Europe and was carried out at the TRACE Lab at ETH Zurich (Toyota Research on Automated Cars in Europe - Zurich).

Owner
Anton Obukhov
CV+ML PhD student with industrial past. Every fork is for a reason.
Anton Obukhov
Transport Mode detection - can detect the mode of transport with the help of features such as acceeration,jerk etc

title emoji colorFrom colorTo sdk app_file pinned Transport_Mode_Detector 🚀 purple yellow gradio app.py false Configuration title: string Display tit

Nishant Rajadhyaksha 3 Jan 16, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Rotate-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Section I. Description The codes are

xinzelee 90 Dec 13, 2022
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

Ziyue Feng 72 Dec 09, 2022
NPBG++: Accelerating Neural Point-Based Graphics

[CVPR 2022] NPBG++: Accelerating Neural Point-Based Graphics Project Page | Paper This repository contains the official Python implementation of the p

Ruslan Rakhimov 57 Dec 03, 2022
A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

TorchArrow (Warning: Unstable Prototype) This is a prototype library currently under heavy development. It does not currently have stable releases, an

Facebook Research 536 Jan 06, 2023
KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

80 Dec 27, 2022
Running AlphaFold2 (from ColabFold) in Azure Machine Learning

Running AlphaFold2 (from ColabFold) in Azure Machine Learning Colby T. Ford, Ph.D. Companion repository for Medium Post: How to predict many protein s

Colby T. Ford 3 Feb 18, 2022
Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation

Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation Overview This example will show how to validate the status of our firewall before and a

Calvin Remsburg 1 Jan 07, 2022
Complete* list of autonomous driving related datasets

AD Datasets Complete* and curated list of autonomous driving related datasets Contributing Contributions are very welcome! To add or update a dataset:

Daniel Bogdoll 13 Dec 19, 2022
Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI This is my personal projects for the course. The course covers deep l

Engen 1.5k Jan 07, 2023
PolyTrack: Tracking with Bounding Polygons

PolyTrack: Tracking with Bounding Polygons Abstract In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segme

Gaspar Faure 13 Sep 15, 2022
An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

Facebook Research 253 Jan 06, 2023
Image Deblurring using Generative Adversarial Networks

DeblurGAN arXiv Paper Version Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our netwo

Orest Kupyn 2.2k Jan 01, 2023
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

Anshul Paigwar 114 Dec 29, 2022
Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

IDRL 330 Jan 07, 2023
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open

19.4k Jan 04, 2023
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

9.6k Jan 06, 2023
Implementation of the Remixer Block from the Remixer paper, in Pytorch

Remixer - Pytorch Implementation of the Remixer Block from the Remixer paper, in Pytorch. It claims that substituting the feedforwards in transformers

Phil Wang 35 Aug 23, 2022
10x faster matrix and vector operations

Bolt is an algorithm for compressing vectors of real-valued data and running mathematical operations directly on the compressed representations. If yo

2.3k Jan 09, 2023
Pytorch based library to rank predicted bounding boxes using text/image user's prompts.

pytorch_clip_bbox: Implementation of the CLIP guided bbox ranking for Object Detection. Pytorch based library to rank predicted bounding boxes using t

Sergei Belousov 50 Nov 27, 2022