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semi-supervised-learning x

15 search results found

Good Papers ⭐ 231

I 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

Adversarial Autoencoders ⭐ 227

Tensorflow implementation of Adversarial Autoencoders

Pro Gnn ⭐ 213

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)

Mixmatch Pytorch ⭐ 106

Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)

Sparsely Grouped Gan ⭐ 74

Code for paper "Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation"

Deviation Network ⭐ 64

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection

Adversarialaudioseparation ⭐ 59

Code accompanying the paper "Semi-supervised adversarial audio source separation applied to singing voice extraction"

Semi Memory ⭐ 46

Code on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory

Usss_iccv19 ⭐ 39

Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

Generative_models ⭐ 27

Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE

Semi Supervised Vae ⭐ 25

Semi Supervised Paper Implementation ⭐ 25

Reproduce some methods in semi-supervised papers.

Code for the paper "MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels"

Temporal Ensembling Semi Supervised ⭐ 13

keras implementation of temporal ensembling(semi-supervised learning)

Representation Learning Reading List ⭐ 13

Repository lists useful papers related to representation learning with links to research papers, conference names and year of publish.

Awesome Ml Pu Learning ⭐ 9

A curated list of resources dedicated to Positive Unlabeled(PU) learning ML methods.

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in stri

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