rtanno21609

data-augmentation-review. List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

Failure-Analysis-and-Model-Repairment. Python

bayesianize. Bayesianize: A Bayesian neural network wrapper in pytorch

InnerEye-DeepLearning. Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning

uncertainty-toolbox. Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

fromp. Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"

DomainBed. DomainBed is a suite to test domain generalization algorithms

InvariantRiskMinimization. PyTorch code to run synthetic experiments.

uncertainty-baselines. High-quality implementations of standard and SOTA methods on a variety of tasks.

pytorch-lightning. Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

emukit. A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

pytorch-ewc. Unofficial PyTorch implementation of DeepMind's PNAS 2017 paper "Overcoming Catastrophic Forgetting"

continual-learning. PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

fvcore. Collection of common code that's shared among different research projects in FAIR computer vision team.

MedMNIST. [pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

fairlearn. A Python package to assess and improve fairness of machine learning models.

fairness-in-ml. This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.

Awesome-Learning-with-Label-Noise. A curated list of resources for Learning with Noisy Labels

discriminative-jackknife. Codebase for "Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions", ICML 2020.

robust_loss_pytorch. A pytorch port of google-research/google-research/robust_loss/

deep-learning-uncertainty. Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

sgd-influence. Python codes for influential instance estimation

Med-Noisy-Labels. [NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Foveation-Segmentation. PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images

seldon-core. An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

AdaptiveNeuralTrees. Adaptive Neural Trees

SPTAG. A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

Rectified-Gradient. Official repository for "Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps".

NiftyNet. An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

voxelmorph. Unsupervised Learning for Image Registration

Deeper-Image-Quality-Transfer-Training-Low-Memory-Neural-Networks-for-3D-Images. An illustration of the code from our MICCAI 2018 paper

TensorFlow_Center_Loss. Center Loss implementation with TensorFlow