JCLALJCLAL is a general purpose framework developed in Java for Active Learning.
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DeepergnnOfficial PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]
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Gans In ActionCompanion repository to GANs in Action: Deep learning with Generative Adversarial Networks
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Stn OcrCode for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition
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Mixmatch PytorchPytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)
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GanomalyGANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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Stylealign[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition
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DtcSemi-supervised Medical Image Segmentation through Dual-task Consistency
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Ssl4misSemi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
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L2cLearning to Cluster. A deep clustering strategy.
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Acgan PytorchPytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs
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SnowballImplementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)
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SusiSuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
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VoskVOSK Speech Recognition Toolkit
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Semi Supervised PytorchImplementations of various VAE-based semi-supervised and generative models in PyTorch
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IctCode for reproducing ICT ( published in IJCAI 2019)
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Mixmatch PytorchCode for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
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Bible text gcnPytorch implementation of "Graph Convolutional Networks for Text Classification"
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Fewshot gan Unet3dTensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
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Deep Sad PytorchA PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
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DeepaffinityProtein-compound affinity prediction through unified RNN-CNN
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SHOT-pluscode for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"
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DST-CBCImplementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"
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Ali PytorchPyTorch implementation of Adversarially Learned Inference (BiGAN).
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UdaUnsupervised Data Augmentation (UDA)
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Usss iccv19Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019
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Social Media Depression Detector😔 😞 😣 😖 😩 Detect depression on social media using the ssToT method introduced in our ASONAM 2017 paper titled "Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media"
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CleanlabThe standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.
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LadderImplementation of Ladder Network in PyTorch.
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Improvedgan PytorchSemi-supervised GAN in "Improved Techniques for Training GANs"
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Adversarial textCode for Adversarial Training Methods for Semi-Supervised Text Classification
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Alibi DetectAlgorithms for outlier and adversarial instance detection, concept drift and metrics.
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Cct[CVPR 2020] Semi-Supervised Semantic Segmentation with Cross-Consistency Training.
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SeeCode for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition"
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Ssgan TensorflowA Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).
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Good PapersI try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
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AdvsemisegAdversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018
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Imbalanced Semi Self[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
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Accel Brain CodeThe purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
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TapeTasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
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HypergcnNeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
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Fixmatch PytorchUnofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
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Triple GanSee Triple-GAN-V2 in PyTorch: https://github.com/taufikxu/Triple-GAN
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HyperGBMA full pipeline AutoML tool for tabular data
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GrandSource code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"
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DiGCNImplement of DiGCN, NeurIPS-2020
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SSL CR HistoOfficial code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medical Image Analysis (MedIA) Journal, Oct, 2021.
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Sparsely Grouped GanCode for paper "Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation"
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DeFMO[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
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