Alternatives and similar repositories for wilds:

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

• google-research / robustness_metrics
  ☆468Updated 8 months ago
• JordanAsh / badge
  An implementation of the BADGE batch active learning algorithm.
  ☆204Updated 10 months ago
• wgrathwohl / JEM
  Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"
  ☆421Updated 2 years ago
• frederick0329 / TracIn
  Implementation of Estimating Training Data Influence by Tracing Gradient Descent (NeurIPS 2020)
  ☆228Updated 3 years ago
• kohpangwei / group_DRO
  Distributionally robust neural networks for group shifts
  ☆261Updated 2 years ago
• facebookresearch / InvariantRiskMinimization
  PyTorch code to run synthetic experiments.
  ☆419Updated 3 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
• 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 …
  ☆357Updated 8 months ago
• yewsiang / ConceptBottleneck
  Concept Bottleneck Models, ICML 2020
  ☆196Updated 2 years ago
• microsoft / otdd
  Optimal Transport Dataset Distance
  ☆163Updated 2 years ago
• KevinMusgrave / pytorch-adapt
  Domain adaptation made easy. Fully featured, modular, and customizable.
  ☆371Updated 2 years ago
• hollance / reliability-diagrams
  Reliability diagrams visualize whether a classifier model needs calibration
  ☆148Updated 3 years ago
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆272Updated 3 years ago
• gpleiss / temperature_scaling
  A simple way to calibrate your neural network.
  ☆1,138Updated 3 years ago
• p-lambda / verified_calibration
  Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…
  ☆148Updated 2 years ago
• wetliu / energy_ood
  ☆409Updated 3 years ago
• omegafragger / DDU
  Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
  ☆139Updated last year
• google / uncertainty-baselines
  High-quality implementations of standard and SOTA methods on a variety of tasks.
  ☆1,498Updated this week
• f-dangel / backpack
  BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
  ☆577Updated 3 months ago
• YyzHarry / SubpopBench
  [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
  ☆107Updated last year
• cfchen-duke / ProtoPNet
  This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpreta…
  ☆361Updated 2 years ago
• bethgelab / model-vs-human
  Benchmark your model on out-of-distribution datasets with carefully collected human comparison data (NeurIPS 2021 Oral)
  ☆343Updated 8 months ago
• jcpeterson / cifar-10h
  Original dataset release for CIFAR-10H
  ☆82Updated 4 years ago
• torrvision / focal_calibration
  Code for the paper "Calibrating Deep Neural Networks using Focal Loss"
  ☆160Updated last year
• decile-team / distil
  DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.
  ☆149Updated 2 years ago
• Continvvm / continuum
  A clean and simple data loading library for Continual Learning
  ☆428Updated last year
• adebayoj / sanity_checks_saliency
  ☆109Updated 2 years ago
• vturrisi / solo-learn
  solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
  ☆1,473Updated last week
• markus93 / NN_calibration
  Calibration of Convolutional Neural Networks
  ☆161Updated last year
• zhiCHEN96 / ConceptWhitening
  ☆120Updated 3 years ago