pytorch-a2c-ppo-acktr 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). Please use hyper parameters from this readme. With other hyper parameters things might not work (it’s RL after all)! This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version of A3C Proximal Policy Optimization PPO Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation ACKTR Generative […]

Res2Net The official pytorch implemention of the paper “Res2Net: A New Multi-scale Backbone Architecture” Our paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-likeconnections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the rangeof receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models,e.g. , […]

NMC PyTorch implementation for paper Neural Marching Cubes, Zhiqin Chen, Hao Zhang. Citation If you find our work useful in your research, please consider citing: @article{chen2021nmc, title={Neural Marching Cubes}, author={Zhiqin Chen and Hao Zhang}, journal={arXiv preprint arXiv:2106.11272}, year={2021} } Notice We have implemented Neural Dual Contouring (NDC).NDC is based on Dual Contouring and thus much easier to implement than NMC.It produces less triangles and vertices (1/8 of NMC, 1/4 of NMC-lite, ≈MC33), with better triangle quality.It runs faster than NMC […]

GAN Ensembling Ensembling with Deep Generative Views.Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard ZhangCVPR 2021 Prerequisites Linux Python 3 NVIDIA GPU + CUDA CuDNN Table of Contents: Colab – run a limited demo version without local installation Setup – download required resources Quickstart – short demonstration code snippet Notebooks – jupyter notebooks for visualization Pipeline – details on full pipeline We project an input image into the latent space of a pre-trained GAN and perturb it slightly to […]

Honk: CNNs for Keyword Spotting Honk is a PyTorch reimplementation of Google’s TensorFlow convolutional neural networks for keyword spotting, which accompanies the recent release of their Speech Commands Dataset. For more details, please consult our writeup: Raphael Tang, Jimmy Lin. Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting. arXiv:1710.06554, October 2017. Raphael Tang, Jimmy Lin. Deep Residual Learning for Small-Footprint Keyword Spotting. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5479-5483. […]

PyTorch-Pose PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options for the most popular human pose databases (e.g., the MPII human pose, LSP and FLIC). Some codes for data preparation and augmentation are brought from the Stacked hourglass network. Thanks to the original author. Update: this repository is compatible with PyTorch 0.4.1/1.0 now! Features Multi-thread data […]

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra EMNLP 2017 (Best Short Paper) If you find this code useful, please consider citing the original work by authors: @inproceedings{visdial, title = {{N}atural {L}anguage {D}oes {N}ot {E}merge ‘{N}aturally’ in {M}ulti-{A}gent {D}ialog}, author = {Satwik Kottur and Jos’e M.F. Moura and Stefan Lee and Dhruv Batra}, journal = {CoRR}, volume = {abs/1706.08502}, […]

Tacotron-pytorch A pytorch implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model. Data I used LJSpeech dataset which consists of pairs of text script and wav files. The complete dataset (13,100 pairs) can be downloaded here. I referred https://github.com/keithito/tacotron for the preprocessing code. File description hyperparams.py includes all hyper parameters that are needed. data.py loads training data and preprocess text to index and wav files to spectrogram. Preprocessing codes for text is in text/ directory. module.py contains all methods, including […]

UMS for Multi-turn Response Selection PyTorch Implementation for AAAI’21 “Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection” Implements the model described in the following paper Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection. @inproceedings{whang2021ums, title={Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection}, author={Whang, Taesun and Lee, Dongyub and Oh, Dongsuk and Lee, Chanhee and Han, Kijong and Lee, Dong-hun […]

radar-to-lidar-place-recognition This page is the implementation of a pre-print, implemented by PyTorch. If you have some questions on this project, please feel free to contact Huan Yin [email protected] . Method Data The files in matlab/RobotCar_data and matlab/MulRan_data can help you generate scancontext of radar and lidar submaps. Also, the generation of lidar submaps is included. Training The train_disco_lidar_quad.py is used for training lidar-to-lidar DiSCO. The train_disco_radar_quad.py is used for training radar-to-radar DiSCO. The train_joint_radar_lidar.py is used for training L2L, R2R […]