AadSah

Hierarchical-Visualization-of-Decisions-using-NBDT

Qualitative visual explanations of intermediate decisions made by Convolutional Neural Network-based Image Classifiers.

bona-fide

A Plagiarism Detector Software.

adapter-transformers

Huggingface Transformers + Adapters = ❤️

Apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

BA3US

code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

class-balanced-loss

Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

convnet-aig

PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

CS231n

My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition

DDA

Code release for "Dynamic Domain Adaptation for Efficient Inference" (CVPR 2021)

Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

DomainBiasMitigation

DSBN

Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"

DynamicRouting

Learning Dynamic Routing for Semantic Segmentation

fixmatch

A simple method to perform semi-supervised learning with limited data.

FLAN

FortuneAlgo

C++ implementation of Fortune algorithm for Voronoi diagram construction

ImbalanceCycleGAN

This repository contains code for the paper "Mitigating Dataset Imbalance via Joint Generation and Classification", published at ECCV-2020 Workshop on Imbalance Problems in Computer Vision (IPCV).

imgaug

Image augmentation for machine learning experiments.

Mini-Conf

Run a conference from your backyard.

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

MM-SADA_Domain_Adaptation_Splits

This repository contains the annotations used for evaluating Unsupervised Domain Adaptation on EPIC Kitchens, with individual kitchens used as seperate domains. The experimental setup can be found in the Multi-modal Domain Adaptation for Fine-Grained Action Recognition (MM-SADA) publication.

mnist-m

The MNIST-M dataset for domain adaptation (PyTorch).

neural-backed-decision-trees

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

pytorch-cnn-visualizations

Pytorch implementation of convolutional neural network visualization techniques

pytorch-grad-cam

PyTorch implementation of Grad-CAM

pytorch-i3d-feature-extraction

Code for I3D Feature Extraction

RISE

Detect model's attention

spottune

SpotTune: Transfer Learning through Adaptive Fine-tuning

Tic-tac-toe

under development