An implementation of the efficient attention module.

Overview

Efficient Attention

An implementation of the efficient attention module.

Description

Efficient attention is an attention mechanism that substantially optimizes the memory and computational efficiency while retaining exactly the same expressive power as the conventional dot-product attention. The illustration above compares the two types of attention. The efficient attention module is a drop-in replacement for the non-local module (Wang et al., 2018), while it:

  • uses less resources to achieve the same accuracy;
  • achieves higher accuracy with the same resource constraints (by allowing more insertions); and
  • is applicable in domains and models where the non-local module is not (due to resource constraints).

Resources

YouTube:

bilibili (for users in Mainland China):

Implementation details

This repository implements the efficient attention module with softmax normalization, output reprojection, and residual connection.

Features not in the paper

This repository implements additionally implements the multi-head mechanism which was not in the paper. To learn more about the mechanism, refer to Vaswani et al.

Citation

The paper will appear at WACV 2021. If you use, compare with, or refer to this work, please cite

@inproceedings{shen2021efficient,
    author = {Zhuoran Shen and Mingyuan Zhang and Haiyu Zhao and Shuai Yi and Hongsheng Li},
    title = {Efficient Attention: Attention with Linear Complexities},
    booktitle = {WACV},
    year = {2021},
}
Owner
Shen Zhuoran
Software Engineer @ponyai (Pony.ai). Alumnus of HKU and @google AI Residency, Diamond player of StarCraft II.
Shen Zhuoran
Codes for [NeurIPS'21] You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership.

You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership Codes for [NeurIPS'21] You are caught stealing my winni

VITA 8 Nov 01, 2022
Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, Pattern Recognition

USDAN The implementation of Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, which is accepte

11 Nov 03, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker

Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker This is a full project of image segmentation using the model built with

Htin Aung Lu 1 Jan 04, 2022
RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems This is our implementation for the paper: Weibo Gao, Qi Liu*, Zhenya Hu

BigData Lab @USTC 中科大大数据实验室 10 Oct 16, 2022
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., de

Jie Huang 14 Oct 21, 2022
PyTorch implementation of neural style randomization for data augmentation

README Augment training images for deep neural networks by randomizing their visual style, as described in our paper: https://arxiv.org/abs/1809.05375

84 Nov 23, 2022
maximal update parametrization (µP)

Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) Paper link | Blog link In Tensor Programs V: Tuning Large Neural Networks

Microsoft 694 Jan 03, 2023
Split your patch similarly to `git add -p` but supporting multiple buckets

split-patch.py This is git add -p on steroids for patches. Given a my.patch you can run ./split-patch.py my.patch You can choose in which bucket to p

102 Oct 06, 2022
Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

Shortformer This repository contains the code and the final checkpoint of the Shortformer model. This file explains how to run our experiments on the

Ofir Press 138 Apr 15, 2022
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

52 Dec 23, 2022
Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds Björn Michele1), Alexandre Boulch1), Gilles Puy1), Maxime Bucher1) and Rena

valeo.ai 15 Dec 22, 2022
FasterAI: A library to make smaller and faster models with FastAI.

Fasterai fasterai is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks

Nathan Hubens 193 Jan 01, 2023
Face Depixelizer based on "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models" repository.

NOTE We have noticed a lot of concern that PULSE will be used to identify individuals whose faces have been blurred out. We want to emphasize that thi

Denis Malimonov 2k Dec 29, 2022
PyTorch implementations of deep reinforcement learning algorithms and environments

Deep Reinforcement Learning Algorithms with PyTorch This repository contains PyTorch implementations of deep reinforcement learning algorithms and env

Petros Christodoulou 4.7k Jan 04, 2023
Official repository for the paper F, B, Alpha Matting

FBA Matting Official repository for the paper F, B, Alpha Matting. This paper and project is under heavy revision for peer reviewed publication, and s

Marco Forte 404 Jan 05, 2023
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) Hang Zhou, Yasheng Sun, Wayne Wu, Chen Cha

Hang_Zhou 628 Dec 28, 2022
Experiments for Fake News explainability project

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

Lorenzo Flores (Lj) 1 Dec 03, 2022
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

69 Dec 10, 2022