Alternatives and similar repositories for INNLab

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

• vislearn / analyzing_inverse_problems
  Code for the paper "Analyzing inverse problems with invertible neural networks." (2018)
  ☆97Updated 5 years ago
• xqding / RealNVP
  A PyTorch Implementation of Density Estimation Using Real NVP
  ☆74Updated 4 years ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆52Updated 11 months ago
• jmclong / random-fourier-features-pytorch
  Implementation of "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" by Tancik et al.
  ☆97Updated last week
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆97Updated 3 years ago
• ermongroup / sliced_score_matching
  Code for reproducing results in the sliced score matching paper (UAI 2019)
  ☆148Updated 5 years ago
• Typal-Research / jacobian_free_backprop
  Implicit networks can be trained efficiently and simply by using Jacobian-free Backprop (JFB).
  ☆37Updated 3 years ago
• nasimrahaman / SpectralBias
  Code for "On the Spectral Bias of Neural Networks", to appear in ICML 2019 (Long Beach, CA).
  ☆108Updated 6 years ago
• vislearn / conditional_INNs
  Code for the paper "Guided Image Generation with Conditional Invertible Neural Networks" (2019)
  ☆98Updated 4 years ago
• patrick-kidger / torchcubicspline
  Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, an…
  ☆252Updated 2 years ago
• 5yearsKim / Conditional-Normalizing-Flow
  Conditional Generative model (Normalizing Flow) and experimenting style transfer using this model
  ☆75Updated 2 years ago
• vislearn / HINT
  Code for the research paper "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference".
  ☆21Updated 4 years ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆128Updated 9 months ago
• yang-song / score_flow
  Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
  ☆144Updated 3 years ago
• cfinlay / ffjord-rnode
  Regularized Neural ODEs (RNODE)
  ☆82Updated 3 years ago
• malbergo / stochastic-interpolants
  ☆152Updated last year
• noegroup / stochastic_normalizing_flows
  Stochastic Normalizing Flows
  ☆76Updated 3 years ago
• oxcsml / riemannian-score-sde
  Score-based generative models for compact manifolds
  ☆111Updated last year
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆170Updated 3 years ago
• tonyduan / normalizing-flows
  Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.
  ☆282Updated last year
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆47Updated 2 years ago
• GavinKerrigan / functional_diffusion
  ☆25Updated 2 years ago
• zabaras / inn-surrogate
  Solving inverse problems using conditional invertible neural networks.
  ☆33Updated 4 years ago
• CW-Huang / sdeflow-light
  A minimalist implementation of score-based diffusion model
  ☆127Updated 3 years ago
• xuzhiqin1990 / F-Principle
  code to show F-Principle in the DNN training
  ☆59Updated 2 years ago
• ghliu / SB-FBSDE
  Likelihood Training of Schrödinger Bridge using FBSDEs Theory, ICLR 2022
  ☆84Updated 3 years ago
• chrischute / real-nvp
  Implementation of Real NVP in PyTorch
  ☆232Updated 5 years ago
• acids-ircam / pytorch_flows
  Implementation and tutorials of normalizing flows with the novel distributions module
  ☆167Updated 4 years ago
• vdeborto / cdsb
  ☆23Updated 2 years ago
• johannbrehmer / manifold-flow
  Manifold-learning flows (ℳ-flows)
  ☆229Updated 4 years ago