policy-adaptation-during-deployment
Ne2Ne-Image-Denoising
| policy-adaptation-during-deployment | Ne2Ne-Image-Denoising | |
|---|---|---|
| 1 | 1 | |
| 109 | 27 | |
| - | - | |
| 1.8 | 3.0 | |
| over 3 years ago | 11 months ago | |
| Python | Python | |
| - | - |
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policy-adaptation-during-deployment
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Exploring Self-Supervised Policy Adaptation To Continue Training After Deployment Without Using Any Rewards
Code: https://github.com/nicklashansen/policy-adaptation-during-deployment
Ne2Ne-Image-Denoising
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Neighbour2Neighbour: The new self-supervised Image Denoising training
It gives outstanding denoising performance with just 300 training images. Have a look at these image results and minimal network implementation here: https://github.com/neeraj3029/Ne2Ne-Image-Denoising
What are some alternatives?
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
SwinIR - SwinIR: Image Restoration Using Swin Transformer (official repository)
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
lightly - A python library for self-supervised learning on images.
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
es_pytorch - High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)