pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

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Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.

point-cloud deep-learning classification segmentation 3d-shape

frustum-pointnets - Frustum PointNets for 3D Object Detection from RGB-D Data

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Created by Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc. This repository is code release for our CVPR 2018 paper (arXiv report here). In this work, we study 3D object detection from RGB-D data. We propose a novel detection pipeline that combines both mature 2D object detectors and the state-of-the-art 3D deep learning techniques. In our pipeline, we firstly build object proposals with a 2D detector running on RGB images, where each 2D bounding box defines a 3D frustum region. Then based on 3D point clouds in those frustum regions, we achieve 3D instance segmentation and amodal 3D bounding box estimation, using PointNet/PointNet++ networks (see references at bottom).

object-detection 3d point-cloud robotics deep-learning

pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

point-cloud classification segmentation neural-network tensorflow geometry-processing

Open3D-PointNet2-Semantic3D - Semantic3D segmentation with Open3D and PointNet++

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Demo project for Semantic3D (semantic-8) segmentation with Open3D and PointNet++. The purpose of this project is to showcase the usage of Open3D in deep learning pipelines and provide a clean baseline implementation for semantic segmentation on Semantic3D dataset. Here's our entry on the semantic-8 test benchmark page. Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community.

tensorflow point-cloud classification pointnet pointnet2 open3d

torch-points3d - Pytorch framework for doing deep learning on point clouds.

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This is a framework for running common deep learning models for point cloud analysis tasks against classic benchmark. It heavily relies on Pytorch Geometric and Facebook Hydra. The framework allows lean and yet complex model to be built with minimum effort and great reproducibility. It also provide a high level API to democratize deep learning on pointclouds. See our paper at 3DV for an overview of the framework capacities and benchmarks of state-of-the-art networks.

deep-learning point-cloud pytorch segmentation scannet pointnet s3dis minkowskiengine kpconv

Repo-2017 - Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

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Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocation

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

•    Jupyter

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Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

tensorflow word-embeddings gru autoencoder gans doc2vec skip-thoughts adagrad cyclegan deep-learning-mathematics capsule-network few-shot-learning quick-thought deep-learning-scratch nadam deep-learning-math lstm-math cnn-math rnn-derivation contractive-autonencoders

tensorflow-tutorial - TensorFlow and Deep Learning Tutorials

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Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnn

Autoencoders - Torch implementations of various types of autoencoders

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Different models can be chosen using th main.lua -model <modelName>. The denoising criterion can be used to replace the standard (autoencoder) reconstruction criterion by using the denoising flag. For example, a denoising AAE (DAAE) [10] can be set up using th main.lua -model AAE -denoising. The corruption process is additive Gaussian noise *~ N(0, 0.5)*.

autoencoder deep-learning

TensorFlow-VAE-GAN-DRAW - A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation)

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TensorFlow implementation of Deep Convolutional Generative Adversarial Networks, Variational Autoencoder (also Deep and Convolutional) and DRAW: A Recurrent Neural Network For Image Generation. Deep Convolutional Generative Adversarial Networks produce decent results after 10 epochs using default parameters.

tensorflow draw recurrent-neural-networks gan vae

NVAE - The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)

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NVAE is a deep hierarchical variational autoencoder that enables training SOTA likelihood-based generative models on several image datasets. These datasets will be downloaded automatically, when you run the main training for NVAE using train.py for the first time. You can use --data=$DATA_DIR/mnist or --data=$DATA_DIR/cifar10, so that the datasets are downloaded to the corresponding directories.

Pointnet2.PyTorch - A faster implementation of PointNet++ based on PyTorch.

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Here I provide a simple example to use this library in the task of KITTI ourdoor foreground point cloud segmentation, and you could refer to the paper PointRCNN for the details of task description and foreground label generation.

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

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Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

outlier-detection anomaly-detection outlier-ensembles outliers anomaly machine-learning data-mining unsupervised-learning python2 python3 fraud-detection autoencoder neural-networks deep-learning

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

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NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

tensorflow distributed ml neural-network python2 python3 pip deep-neural-networks deep-learning convolutional-neural-networks medical-imaging medical-image-computing medical-image-processing medical-images segmentation gan autoencoder medical-image-analysis image-guided-therapy

zhihu - This repo contains the source code in my personal column (https://zhuanlan

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This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translation

AugmentedAutoencoder - Official Code: Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

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Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, Rudolph Triebel Best Paper Award, ECCV 2018. We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries.

keras-molecules - Autoencoder network for learning a continuous representation of molecular structures

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We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

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In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-network

TensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

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텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inception

SoTA-Point-Cloud - 🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

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🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

semantic-segmentation pointclouds 3d-segmentation instance-segmentation 3d-deep-learning 3d-detection 3d-classification 3d-tracking