Alternatives and similar repositories for robustness_metrics:

Users that are interested in robustness_metrics are comparing it to the libraries listed below

• p-lambda / wilds
  A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
  ☆559Updated last year
• team-approx-bayes / dl-with-bayes
  Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"
  ☆244Updated 5 years ago
• wgrathwohl / JEM
  Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"
  ☆420Updated 2 years ago
• google / uncertainty-baselines
  High-quality implementations of standard and SOTA methods on a variety of tasks.
  ☆1,495Updated last month
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆272Updated 3 years ago
• wjmaddox / swa_gaussian
  Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
  ☆457Updated last year
• SamsungLabs / pytorch-ensembles
  Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
  ☆235Updated 2 years ago
• f-dangel / backpack
  BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
  ☆572Updated 2 months ago
• p-lambda / verified_calibration
  Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…
  ☆145Updated 2 years ago
• aangelopoulos / conformal_classification
  Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…
  ☆236Updated 2 years ago
• torrvision / focal_calibration
  Code for the paper "Calibrating Deep Neural Networks using Focal Loss"
  ☆160Updated last year
• OATML / bdl-benchmarks
  Bayesian Deep Learning Benchmarks
  ☆668Updated last year
• f-dangel / cockpit
  Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
  ☆474Updated 2 years ago
• frederick0329 / TracIn
  Implementation of Estimating Training Data Influence by Tracing Gradient Descent (NeurIPS 2020)
  ☆227Updated 3 years ago
• EFS-OpenSource / calibration-framework
  The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a …
  ☆354Updated 7 months ago
• izmailovpavel / understandingbdl
  ☆240Updated 2 years ago
• decile-team / cords
  Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using co…
  ☆328Updated last year
• aleximmer / Laplace
  Laplace approximations for Deep Learning.
  ☆498Updated last month
• markus93 / NN_calibration
  Calibration of Convolutional Neural Networks
  ☆160Updated last year
• craffel / dl3d-seminar
  Course webpage for COMP 790, (Deep) Learning from Limited Labeled Data
  ☆304Updated 4 years ago
• SirRob1997 / Crowded-Valley---Results
  This repository contains the results for the paper: "Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers"
  ☆180Updated 3 years ago
• AlaaLab / deep-learning-uncertainty
  Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…
  ☆618Updated 2 years ago
• y0ast / DUE
  Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".
  ☆111Updated 2 years ago
• TorchDrift / TorchDrift
  Drift Detection for your PyTorch Models
  ☆315Updated 2 years ago
• noahgolmant / pytorch-hessian-eigenthings
  Efficient PyTorch Hessian eigendecomposition tools!
  ☆370Updated last year
• y0ast / slurm-for-ml
  A Machine Learning workflow for Slurm.
  ☆149Updated 4 years ago
• facebookresearch / open_lth
  A repository in preparation for open-sourcing lottery ticket hypothesis code.
  ☆629Updated 2 years ago
• IBM / UQ360
  Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty i…
  ☆259Updated 7 months ago
• hollance / reliability-diagrams
  Reliability diagrams visualize whether a classifier model needs calibration
  ☆146Updated 3 years ago
• gpleiss / temperature_scaling
  A simple way to calibrate your neural network.
  ☆1,130Updated 3 years ago