Articles About Machine Learning

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch. Torchmeta contains popular meta-learning benchmarks, fully compatible with both torchvision and PyTorch’s DataLoader. Features A unified interface for both few-shot classification and regression problems, to allow easy benchmarking on multiple problems and reproducibility. Helper functions for some popular problems, with default arguments from the literature. An thin extension of PyTorch’s Module, called MetaModule, that simplifies the creation of certain meta-learning models (e.g. gradient based meta-learning methods). See […]

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Sign-Agnostic Optimization of Convolutional Occupancy Networks

This repository contains the implementation of the paper: Sign-Agnostic CONet: Learning Implicit Surface Reconstructions by Sign-Agnostic Optimization of Convolutional Occupancy NetworksICCV 2021 (Oral) If you find our code or paper useful, please consider citing @inproceedings{tang2021sign, title={SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks}, author={Tang, Jiapeng and Lei, Jiabao and Xu, Dan and Ma, Feiying and Jia, Kui and Zhang, Lei}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, year={2021} } Contact Jiapeng Tang for questions, comments and reporting bugs. Installation […]

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Learning a Contact Potential Field to Model the Hand-Object Interaction

This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-Object InteractionLixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, Cewu LuICCV 2021 1. Get our code: $ git clone –recursive https://github.com/lixiny/CPF.git $ cd CPF 2. Set up your new environment: $ conda env create -f environment.yaml $ conda activate cpf 3. Download asset files Down load our [assets.zip] and unzip it as an assets/ folder. Download the MANO […]

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Adversarial Training Against Location-Optimized Adversarial Patches

Adversarial-Patch-Training Code for the paper: Sukrut Rao, David Stutz, Bernt Schiele. (2020) Adversarial Training Against Location-Optimized Adversarial Patches. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_32 Setup Requirements Python 3.7 or above PyTorch scipy h5py scikit-image scikit-learn Optional requirements To use script to convert data to HDF5 format torchvision Pillow pandas To use Tensorboard logging With the exception of Python and PyTorch, all requirements […]

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Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

The implement of paper “Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization” Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-artrecommendation performance. In the ubiquitous implicit feedback based CF, users’ unobserved behaviors are treated as unlinked edges in the user-item bipartite graph.As users’ unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference […]

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Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Image Super-Resolution Using Very Deep Residual Channel Attention Networks This repository is for RCAN introduced in the following paper Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu, “Image Super-Resolution Using Very Deep Residual Channel Attention Networks”, ECCV 2018, [arXiv] The code is built on EDSR (PyTorch) and tested on Ubuntu 14.04/16.04 environment (Python3.6, PyTorch_0.4.0, CUDA8.0, cuDNN5.1) with Titan X/1080Ti/Xp GPUs. RCAN model has also been merged into EDSR (PyTorch). Introduction Convolutional neural network (CNN) depth […]

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Pixel-level self-paced learning for super-resolution

This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resolution, which has been accepted by ICASSP 2020. [arxiv][PDF] trained model files: Baidu Pan(code: v0be) Requirements This code is forked from thstkdgus35/EDSR-PyTorch. In the light of its README, following libraries are required: Python 3.6+ (Python 3.7.0 in my experiments) PyTorch >= 1.0.0 (1.1.0 in my experiments) numpy skimage imageio matplotlib tqdm Core Parts Detail code can be found in Loss.forward, which can be simplified as: # take L1 […]

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A Machine Learning model which predicts the presence of Diabetes in Patients

This is a machine Learning mode which tries to determine if a person has a heart disease or not. Data The dataset is in comma seperated values (.csv) format and is included in th code. Packages Used The following Packages were used scikit-learn: To preprocess the data, initiate the model, split the data, cross-validate the data and score the model. pandas: To import the dataset, change the dataset into a dataframe and view the data seaborn & matplotlib: To visualize […]

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Large scale embeddings on a single machine

Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs requires a large amount of data movement to get embedding parameters from storage to the computational device.Marius is designed to mitigate/reduce data movement overheads using: Pipelined training and IO Partition caching and buffer-aware data orderings Details on how Marius works can be found in our OSDI ’21 Paper, where experiment scripts and configurations can be found in the […]

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Benchmarking Model and System Performance of Federated Learning

FedScale This repository contains scripts and instructions of building FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at […]

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