Revealing and Protecting Labels in Distributed Training

Related tags

Deep Learning learning-bag-of-words
Overview

This repo stores the official code for the paper

@inproceedings{dang2021revealing,
 author = {Dang, Trung and Thakkar, Om and Ramaswamy, Swaroop and Mathews, Rajiv and Chin, Peter and Beaufays, Françoise},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Revealing and Protecting Labels in Distributed Training},
 url = {},
 year = {2021}
}

Instructions

Reveal image labels

cd image_recognition
python export_gradients.py --model ResNet50 --batch_size 10
python rlg.py --model ResNet50 --batch_size 10

Reveal speech transcript

This will be uploaded soon.

You might also like...
Secure Distributed Training at Scale

Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

Yandex Research 9 Jul 11, 2022
Distributed Arcface Training in Pytorch
Distributed Arcface Training in Pytorch

Distributed Arcface Training in Pytorch

null 3 Nov 23, 2021
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL labels about 43 hours of mocap sequences from AMASS [1] with action labels.

null 113 Dec 28, 2022
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022
This is an unofficial PyTorch implementation of Meta Pseudo Labels

This is an unofficial PyTorch implementation of Meta Pseudo Labels. The official Tensorflow implementation is here.

Jungdae Kim 320 Jan 8, 2023
The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

Inferring Spatial Uncertainty in Object Detection A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in

ZINING WANG 21 Mar 3, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

null 18 Nov 8, 2022
A curated (most recent) list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Jiaheng Wei 321 Jan 9, 2023
TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels.
TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels.

AutoDSP TLDR; Train custom adaptive filter optimizers without hand tuning or extra labels. About Adaptive filtering algorithms are commonplace in sign

Jonah Casebeer 48 Sep 19, 2022
Owner
Google Interns
Google Interns
GitHub
PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

Contrast to Divide: self-supervised pre-training for learning with noisy labels This is an official implementation of "Contrast to Divide: self-superv

null 55 Nov 23, 2022
Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

null 43 Dec 26, 2022
This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their

Liron Bdolah 8 May 22, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intention of Apex is to make up-to-date utilities available to users as quickly as possible.

NVIDIA Corporation 6.9k Jan 3, 2023
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

Microsoft 8.4k Jan 1, 2023
Bagua is a flexible and performant distributed training algorithm development framework.

Bagua is a flexible and performant distributed training algorithm development framework.

null 786 Dec 17, 2022
The pure and clear PyTorch Distributed Training Framework.

The pure and clear PyTorch Distributed Training Framework. Introduction Requirements and Usage Dependency Dataset Basic Usage Slurm Cluster Usage Base

WILL LEE 208 Dec 20, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.

English | 简体中文 Easy Parallel Library Overview Easy Parallel Library (EPL) is a general and efficient library for distributed model training. Usability

Alibaba 185 Dec 21, 2022
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and testing data for various deep learning projects such as 6D object pose estimation projects singleshotpose, as well as object detection and instance segmentation projects.

null 305 Dec 16, 2022