Deep CORAL A PyTorch implementation of ‘Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016’ Deep CORAL can learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). My implementation result (Task Amazon -> Webcam): Requirement Usage Unzip dataset in dataset/office31.tar.gz Run python3 main.py GitHub https://github.com/SSARCandy/DeepCORAL    

Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning [1]. Results and pretrained models can be found in the releases. [x] DQN [2] [x] Double DQN [3] [x] Prioritised Experience Replay [4] [x] Dueling Network Architecture [5] [x] Multi-step Returns [6] [x] Distributional RL [7] [x] Noisy Nets [8] Run the original Rainbow with the default arguments: python main.py Data-efficient Rainbow [9] can be run using the following options (note that the “unbounded” memory is implemented here in practice by manually […]

SPPR Self-Promoted Prototype Refinement for Few-Shot Class-Incremental LearningThis is the implementation of the paper “Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning” (accepted to CVPR2021). Requirements Python 3.8 PyTorch 1.8.1 (>1.1.0) cuda 11.2 Preparing Few-Shot Class-Incremental Learning Datasets Download following datasets: 1. CIFAR-100 Automatically downloaded on torchvision. 2. MiniImageNet (1) Download MiniImageNet train/test images[github],and prepare related datasets according to [TOPIC]. (2) or Download processed data from our Google Drive: [mini-imagenet.zip],(and locate the entire folder under datasets/ directory). 3. CUB200 (1) Download […]

MMF MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. See full list of project inside or built on MMF here. MMF is powered by PyTorch, allows distributed training and is un-opinionated, scalable and fast. Use MMF to bootstrap for your next vision and language multimodal research project by following the installation instructions. Take […]

nmtpytorch Sequence to Sequence Framework in PyTorch This project is not actively maintained so issues created are unlikely to be addressed in a timely way. If you are interested, there’s a recent fork of this repository called pysimt which includes Transformer-based architectures as well. nmtpytorch allows training of various end-to-end neural architectures includingbut not limited to neural machine translation, image captioning and automaticspeech recognition systems. The initial codebase was in Theano and wasinspired from the famous dl4mt-tutorialcodebase. nmtpytorch received valuable […]

Quick NLP Quick NLP is a deep learning nlp library inspired by the fast.ai library It follows the same api as fastai and extends it allowing for quick and easy running of nlp models Features Python 3.6 code Tight-knit integration with Fast.ai library: Fast.ai style DataLoader objects for sentence to sentence algorithms Fast.ai style DataLoader objects for dialogue algorithms Fast.ai style DataModel objects for training nlp models Can run a seq2seq model with a few lines of code similar to […]

trRosetta2 This package contains deep learning models and related scripts used by Baker group in CASP14. Installation Linux/Mac clone the package git clone https://github.com/RosettaCommons/trRosetta2cd trRosetta2 create conda environment using one of the .yml files: casp14-baker-linux-cpu.yml, casp14-baker-linux-gpu.yml, casp14-baker-mac-cpu.yml conda env create -f casp14-baker-linux-gpu.ymlconda activate casp14-baker download network weights [1.1G] wget https://files.ipd.uw.edu/pub/trRosetta2/weights.tar.bz2tar xf weights.tar.bz2 download and install third-party software ./install_dependencies.sh download sequence and structure databases wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gzmkdir -p UniRef30_2020_06tar xf UniRef30_2020_06_hhsuite.tar.gz -C ./UniRef30_2020_06 wget https://files.ipd.uw.edu/pub/trRosetta2/pdb100_2020Mar11.tar.gztar xf pdb100_2020Mar11.tar.gz Obtain a PyRosetta licence and […]

LapDepth-release This repository is a Pytorch implementation of the paper “Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals” Minsoo Song, Seokjae Lim, and Wonjun Kim*IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) Requirements Python >= 3.7 Pytorch >= 1.6.0 Ubuntu 16.04 CUDA 9.2 cuDNN (if CUDA available) some other packages: geffnet, path, IPython, blessings, progressbar Pretrained models You can download pre-trained model Demo images (Single Test Image Prediction) Make sure you download the pre-trained model and placed it […]

Guide & Examples to create deeplearning gstreamer plugins and use them in your pipeline Thanks to the work done by @jackersson. In this repository we have taken inspirations from: gstreamer-python gst-plugins-tf We have currently tested our code on Ubuntu 18.04. You can also refer to the official installation document for your linux flavor. Packages Required sudo apt install cmake m4 git build-essential sudo apt install libssl-dev libcurl4-openssl-dev liblog4cplus-dev Gstreamer Installation sudo apt-get install libgstreamer1.0-0 gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc […]

Creepypasta – Text Generator Website which uses Deep Learning to generate horror stories. There are two parts to the project. One is the Deep Learning model which generates the text. The other is the website which uses the model to generate text. I have used Deep Learning model to generate text. It is a Neural Network which uses Recurrent Neural Network to generate text. I have hosted a model on Algorithmia and used it’s API to generate text. Built With […]