The friendly PIL fork

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

This repository contains the code for our paper "Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs".

A curated list of awesome datasets with human label variation (un-aggregated labels) in Natural Language Processing and Computer Vision, accompanying The 'Problem' of Human Label Variation: On Grou…

Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".

ICCV2021 Expert-Goal Trajectory Prediction

All papers in this repository are interesting papers regardless of the field. Everything from analytical essays of cultural events to machine learning manuscripts are all valid in this repository. …

List of companies offering Machine learning and Data Science internships

UC-Net: Uncertainty Inspired RGB-D Saliency Detectionvia Conditional Variational Autoencoders, CVPR2020

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

Python code for paper - Variational Deep Embedding : A Generative Approach to Clustering

Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering

A collection of state-of-the-art image quality assessment algorithms

Implementation of the paper "No Reference Image Quality Assessment in the Spatial Domain" by A Mittal et al. in OpenCV (using both C++ and Python)

Replication of "Auto-encoder Based Data Clustering" Song et al

I categorize, annotate and write comments for all research papers I read (430+ papers since 2018).

PyTorch code for softmax variants: center loss, cosface loss, large-margin gaussian mixture, COCOLoss, ring loss

Open source code for paper "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere" ICML 2020

Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.

Dropout As A Bayesian Approximation: Code

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Original dataset release for CIFAR-10H

程序员延寿指南 | A programmer's guide to live longer

Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at https://www.noisylabels.com/.

A curated list of resources for Learning with Noisy Labels

Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"

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

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