A Machine Teaching Framework for Scalable Recognition

Last update: Dec 08, 2021

Related tags

Overview

MEMORABLE

This repository contains the source code accompanying our ICCV 2021 paper.

A Machine Teaching Framework for Scalable Recognition
Pei Wang, Nuno Vasconcelos.
In ICCV, 2021.

@InProceedings{wang2021gradient,
author = {Wang, Pei and Vasconcelos, Nuno},
title = {A Machine Teaching Framework for Scalable Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021}
}

Requirements

The project was implemented and tested in Python 3.5 and Pytorch 1.0. Other versions should work after minor modification.
NVIDIA GPU and cuDNN are required to have fast speeds. For now, CUDA 8.0 with cuDNN 6.0.20 has been tested. The other versions should be working.

Datasets

Butterflies and Chinese Characters, Gull are used. Please organize them as below after download,

datasets
|_ butterflies_crop
  |_ images
    |_ Viceroy
    |_ ...
|_ chinese_chars
  |_ images
    |_ grass
    |_ ...
|_ CUBgull
  |_ images
    |_ CaliforniaGull
    |_ ...

Implementation details

To generate counterfactual explanations

get_all_CE_butterflies.py
get_all_CE_gull.py

by SimCLR model

get_all_CE_butterflies_simclr.py
get_all_CE_gull_simclr.py

To generate teaching images and explanations

train_butterflies_CMaxGrad.py
train_gull_CMaxGrad.py

by SimCLR model

train_butterflies_CMaxGrad_simclr.py
train_gull_CMaxGrad_simclr.py

For the SimCLR, we used repo for training. Thanks for their open-source. For standard model, we trained by

train_butterflies_resnet18.py
train_gull_resnet18_linear.py

For questions, feel free to reach out

Pei Wang: [email protected]

A Machine Teaching Framework for Scalable Recognition

Related tags

Overview

MEMORABLE

Requirements

Datasets

Implementation details

To generate counterfactual explanations

To generate teaching images and explanations

Owner

HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks

Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

Background Matting: The World is Your Green Screen

BankNote-Net: Open dataset and encoder model for assistive currency recognition

A machine learning malware analysis framework for Android apps.

[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.

PECOS - Prediction for Enormous and Correlated Spaces

Joint detection and tracking model named DEFT, or ``Detection Embeddings for Tracking.

Perform zero-order Hankel Transform for an 1D array (float or real valued).

Pytorch implementation of COIN, a framework for compression with implicit neural representations 🌸

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

A clear, concise, simple yet powerful and efficient API for deep learning.

CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation

PlenOctree Extraction algorithm

Official Python implementation of the 'Sparse deconvolution'-v0.3.0

we propose EfficientDerain for high-efficiency single-image deraining

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Style transfer between images was performed using the VGG19 model