Beast code in Giters

Deep learning based one class classification code targeting one class image classification. Tests carried out on Abnormal image detection, Novel image detection and Active Authentication reported state of the art results.

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OCGAN-Pytorch

UnOfficial PyTorch Implementation of OCGAN!

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OOD-detection-using-OECC

Outlier Exposure with Confidence Control for Out-of-Distribution Detection

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Label-Propagation

Scikits.learn style implementation of common label propagation algorithms

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DPC-DLP

Dynamic Graph-Based Label Propagation for Density Peaks Clustering

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Efficient-SSL

Implementation for IGCN and GLP model in our paper "Label Efficient Semi-Supervised Learning via Graph Filtering."

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metric-transfer.pytorch

Deep Metric Transfer for Label Propagation with Limited Annotated Data

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label_propagation

Implementations of label propagation like algorithms

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TPN

Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.

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cdp

Code for our ECCV 2018 work.

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simclr

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

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moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

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caffe-int8-convert-tools

Generate a quantization parameter file for ncnn framework int8 inference

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DepthwiseConvolution

A personal depthwise convolution layer implementation on caffe by liuhao.(only GPU)

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pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

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research-ms-loss

MS-Loss: Multi-Similarity Loss for Deep Metric Learning

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EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

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Person_reID_baseline_pytorch

:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

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DeepID2

use caffe to training a deepid2 net with softmax and contrastive loss, adding identity2verify_layer to translate a single sample into pairwise, thus mix the softmax and contrastive layer together

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DeepID2_based_Inception

this network is built based on Inception. and optimize it by using identification loss and verification loss which derive from DeepID2+

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deep-person-reid

Torchreid: Deep learning person re-identification in PyTorch.

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senet.pytorch

PyTorch implementation of SENet

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