Dataset Condensation with Contrastive Signals

Last update: May 19, 2022

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Overview

Dataset Condensation with Contrastive Signals

This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC).
The implementation is based on (https://github.com/VICO-UoE/DatasetCondensation).

Prerequisites

pytorch (1.2.0)
numpy (1.15.1)
torchvision (0.4.0)
scipy (1.1.0)

Training and evaluation

To train the DCC (or DSAC) model in the paper, run this command:

python main.py --ipc <1, 10, or 50> --model ConvNet --dataset 
   
     (--imagenet_group 
    
     ) --method 
     
       --contrast --save_path

Please download ImageNet32x32 at (https://image-net.org/download-images)

Owner

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Dataset Condensation with Contrastive Signals

Related tags

Overview

Dataset Condensation with Contrastive Signals

Prerequisites

Training and evaluation

Owner

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Dataset Condensation with Contrastive Signals

Related tags

Overview

Dataset Condensation with Contrastive Signals

Prerequisites

Training and evaluation

Owner

Scheduling BilinearRewards

Artificial Intelligence playing minesweeper 🤖

Library for time-series-forecasting-as-a-service.

Code & Models for Temporal Segment Networks (TSN) in ECCV 2016

Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

Deep learning model for EEG artifact removal

A memory-efficient implementation of DenseNets

GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning

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A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

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