Code for paper "Learning to Reweight Examples for Robust Deep Learning"

Overview

learning-to-reweight-examples

Code for paper Learning to Reweight Examples for Robust Deep Learning. [arxiv]

Environment

We tested the code on

  • tensorflow 1.10
  • python 3

Other dependencies:

  • numpy
  • tqdm
  • six
  • protobuf

Installation

The following command makes the protobuf configurations.

make

MNIST binary classification experiment

python -m mnist.mnist_train --exp ours

Please see mnist/mnist_train.py for more options.

CIFAR noisy label experiments

Download CIFAR dataset

bash cifar/download_cifar.sh ./data

Config files are located in cifar/configs. For ResNet-32, use cifar/configs/cifar-resnet-32.prototxt. For Wide ResNet-28, use cifar/configs/cifar-wide-resnet-28-10.prototxt.

CIFAR-10/100 uniform flip noise experiment

python -m cifar.cifar_train --config [CONFIG]

Please see cifar/cifar_train.py for more options.

CIFAR-10/100 background flip noise experiment

python -m cifar.cifar_train_background --config [CONFIG]

Please see cifar/cifar_train_background.py for more options.

Citation

If you use our code, please consider cite the following: Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun. Learning to Reweight Examples for Robust Deep Learning. ICML 2018.

@inproceedings{ren18l2rw,
  author    = {Mengye Ren and Wenyuan Zeng and Bin Yang and Raquel Urtasun},
  title     = {Learning to Reweight Examples for Robust Deep Learning},
  booktitle = {ICML},
  year      = {2018},
}
Owner
Uber Research
Uber's research projects. Projects in this organization are not built for production usage. Maintainance and supports are limited.
Uber Research
This program writes christmas wish programmatically. It is using turtle as a pen pointer draw christmas trees and stars.

Introduction This is a simple program is written in python and turtle library. The objective of this program is to wish merry Christmas programmatical

Gunarakulan Gunaretnam 1 Dec 25, 2021
High-quality implementations of standard and SOTA methods on a variety of tasks.

Uncertainty Baselines The goal of Uncertainty Baselines is to provide a template for researchers to build on. The baselines can be a starting point fo

Google 1.1k Dec 30, 2022
Modeling CNN layers activity with Gaussian mixture model

GMM-CNN This code package implements the modeling of CNN layers activity with Gaussian mixture model and Inference Graphs visualization technique from

3 Aug 05, 2022
[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Efficient Graph Similarity Computation - (EGSC) This repo contains the source code and dataset for our paper: Slow Learning and Fast Inference: Effici

23 Nov 11, 2022
PyTorch 1.0 inference in C++ on Windows10 platforms

Serving PyTorch Models in C++ on Windows10 platforms How to use Prepare Data examples/data/train/ - 0 - 1 . . . - n examples/data/test/

Henson 88 Oct 15, 2022
Image segmentation with private İstanbul Dataset

Image Segmentation This repo was created for academic research and test result. Repo will update after academic article online. This repo contains wei

İrem KÖMÜRCÜ 9 Dec 11, 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

BEAS Blockchain Enabled Asynchronous and Secure Federated Machine Learning Default Network Configuration: The default application uses the HyperLedger

Harpreet Virk 11 Nov 20, 2022
Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.

Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos Introduction Point cloud videos exhibit irregularities and lack of or

Hehe Fan 101 Dec 29, 2022
Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Sara Fridovich-Keil 81 Dec 25, 2022
WarpRNNT loss ported in Numba CPU/CUDA for Pytorch

RNNT loss in Pytorch - Numba JIT compiled (warprnnt_numba) Warp RNN Transducer Loss for ASR in Pytorch, ported from HawkAaron/warp-transducer and a re

Somshubra Majumdar 15 Oct 22, 2022
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution Pytorch implementation of local motion and contrast pr

Xinyi Ying 28 Dec 15, 2022
Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

Dynamic VAE frame Automatic feature extraction can be achieved by probability di

10 Oct 07, 2022
Single-Stage 6D Object Pose Estimation, CVPR 2020

Overview This repository contains the code for the paper Single-Stage 6D Object Pose Estimation. Yinlin Hu, Pascal Fua, Wei Wang and Mathieu Salzmann.

CVLAB @ EPFL 89 Dec 26, 2022
PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

WaveGrad2 - PyTorch Implementation PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis. Status (202

Keon Lee 59 Dec 06, 2022
Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Stephen James 51 Dec 27, 2022
FwordCTF 2021 Infrastructure and Source code of Web/Bash challenges

FwordCTF 2021 You can find here the source code of the challenges I wrote (Web and Bash) in FwordCTF 2021 and the source code of the platform with our

Kahla 5 Nov 25, 2022
N-Person-Check-Checker-Splitter - A calculator app use to divide checks

N-Person-Check-Checker-Splitter This is my from-scratch programmed calculator ap

2 Feb 15, 2022
Image classification for projects and researches

This is a tool to help you quickly solve classification problems including: data analysis, training, report results and model explanation.

Nguyễn Trường Lâu 2 Dec 27, 2021
Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽

The Hands-on Reinforcement Learning course 🚀 From zero to HERO 🦸🏻‍🦸🏽 Out of intense complexities, intense simplicities emerge. -- Winston Churchi

Pau Labarta Bajo 260 Dec 28, 2022
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame

1.4k Jan 07, 2023