Code Release for Learning to Adapt to Evolving Domains

Related tags

Deep LearningEAML
Overview

EAML

Code release for "Learning to Adapt to Evolving Domains" (NeurIPS 2020)

Prerequisites

  • PyTorch >= 0.4.0 (with suitable CUDA and CuDNN version)
  • torchvision >= 0.2.1
  • Python3
  • Numpy
  • argparse
  • PIL

Dataset

Rotated MNIST: https://drive.google.com/file/d/1eaw42sg4Cgm34790AW_SKGCSkFosugl2/view?usp=sharing

Training

EAML 

%run eaml.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-in 0.03 --lr-out 0.003 

JAN 

%run JAN.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-in 0.03 --lr-out 0.003

Source 

%run source.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-out 0.003

Acknowledgement

This code is implemented based on the JAN (Joint Adaptation Networks) code, and it is our pleasure to acknowledge their contributions. The meta-learning code is adapted from https://github.com/dragen1860/MAML-Pytorch/.

Citation

If you use this code for your research, please consider citing:

@inproceedings{NEURIPS2020_fd69dbe2,
 author = {Liu, Hong and Long, Mingsheng and Wang, Jianmin and Wang, Yu},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {22338--22348},
 publisher = {Curran Associates, Inc.},
 title = {Learning to Adapt to Evolving Domains},
 url = {https://proceedings.neurips.cc/paper/2020/file/fd69dbe29f156a7ef876a40a94f65599-Paper.pdf},
 volume = {33},
 year = {2020}
}


Contact

If you have any problem about our code, feel free to contact

Owner
Undergraduate student majoring in electronic engineering
Code for a seq2seq architecture with Bahdanau attention designed to map stereotactic EEG data from human brains to spectrograms, using the PyTorch Lightning.

stereoEEG2speech We provide code for a seq2seq architecture with Bahdanau attention designed to map stereotactic EEG data from human brains to spectro

15 Nov 11, 2022
Recreate CenternetV2 based on MMDET.

Introduction This project is trying to Recreate CenternetV2 based on MMDET, which is proposed in paper Probabilistic two-stage detection. This project

25 Dec 09, 2022
PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

VGPL-Visual-Prior PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner (VGPL). Give

Toru 8 Dec 29, 2022
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
A comprehensive list of published machine learning applications to cosmology

ml-in-cosmology This github attempts to maintain a comprehensive list of published machine learning applications to cosmology, organized by subject ma

George Stein 290 Dec 29, 2022
Generate pixel-style avatars with python.

face2pixel Generate pixel-style avatars with python. Run: Clone the project: git clone https://github.com/theodorecooper/face2pixel install requiremen

Theodore Cooper 2 May 11, 2022
"Segmenter: Transformer for Semantic Segmentation" reproduced via mmsegmentation

Segmenter-based-on-OpenMMLab "Segmenter: Transformer for Semantic Segmentation, arxiv 2105.05633." reproduced via mmsegmentation. We reproduce Segment

EricKani 22 Feb 24, 2022
基于DouZero定制AI实战欢乐斗地主

DouZero_For_Happy_DouDiZhu: 将DouZero用于欢乐斗地主实战 本项目基于DouZero 环境配置请移步项目DouZero 模型默认为WP,更换模型请修改start.py中的模型路径 运行main.py即可 SL (baselines/sl/): 基于人类数据进行深度学习

1.5k Jan 08, 2023
Let Python optimize the best stop loss and take profits for your TradingView strategy.

TradingView Machine Learning TradeView is a free and open source Trading View bot written in Python. It is designed to support all major exchanges. It

Robert Roman 473 Jan 09, 2023
Flexible-Modal Face Anti-Spoofing: A Benchmark

Flexible-Modal FAS This is the official repository of "Flexible-Modal Face Anti-

Zitong Yu 22 Nov 10, 2022
OneFlow is a performance-centered and open-source deep learning framework.

OneFlow OneFlow is a performance-centered and open-source deep learning framework. Latest News Version 0.5.0 is out! First class support for eager exe

OneFlow 4.2k Jan 07, 2023
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

Imbalanced Dataset Sampler Introduction In many machine learning applications, we often come across datasets where some types of data may be seen more

Ming 2k Jan 08, 2023
PyTorch Implementation of Sparse DETR

Sparse DETR By Byungseok Roh*, Jaewoong Shin*, Wuhyun Shin*, and Saehoon Kim at Kakao Brain. (*: Equal contribution) This repository is an official im

Kakao Brain 113 Dec 28, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
This repository is the code of the paper "Sparse Spatial Transformers for Few-Shot Learning".

🌟 Sparse Spatial Transformers for Few-Shot Learning This code implements the Sparse Spatial Transformers for Few-Shot Learning(SSFormers). Our code i

chx_nju 38 Dec 13, 2022
code from "Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity"

Code associated with the paper "Tensor decomposition of higher-order correlations by nonlinear Hebbian learning," Ocker & Buice, Neurips 2021. "plot_f

Gabriel Koch Ocker 4 Oct 16, 2022
Time Delayed NN implemented in pytorch

Pytorch Time Delayed NN Time Delayed NN implemented in PyTorch. Usage kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)] tdnn = TDNN

Daniil Gavrilov 79 Aug 04, 2022
“Robust Lightweight Facial Expression Recognition Network with Label Distribution Training”, AAAI 2021.

EfficientFace Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI

Zengqun Zhao 119 Jan 08, 2023