Unsupervised Data Augmentation (UDA)
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Apr 22, 2020 - Python
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semi-supervised-learning
Here are 256 public repositories matching this topic...
Find label errors in datasets, weak supervision, and learning with noisy labels.
machine-learning
machine-learning-algorithms
weak-supervision
semi-supervised-learning
unsupervised-learning
data-cleaning
clean-data
learning-with-confident-examples
noisy-data
confident-learning
latent-estimation
robust-machine-learning
learning-with-noisy-labels
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Apr 23, 2020 - Python
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
deep-neural-networks
ai
deep-learning
artificial-intelligence
generative-adversarial-network
gan
dcgan
semi-supervised-learning
gans
artificial-intelligence-algorithms
cycle-gan
conditional-gan
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Sep 15, 2019 - Jupyter Notebook
Implementations of various VAE-based semi-supervised and generative models in PyTorch
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Mar 2, 2020 - Python
ran337287
commented
Apr 10, 2018
When I run text_recognition_demo.py with parameters gpu=-1(that is cpu), it can return the results.
But if I set gpu=0, there is something wrong.
The error is
ValueError：numpy and cupy must not be used together type(W):<class 'numpy.ndarray'>, type(x):<class 'cupy.core.core.ndarray'>,type(b):<class 'numpy.ndarray'>
I know nothing about Chainer, what should I do to solve it?
bhaprayan
commented
Apr 14, 2019
It'd be nice if you could add a list of required packages (i.e. h5py and progressbar), that have to be installed.
Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition
deep-learning
mxnet
end-to-end
text-recognition
semi-supervised-learning
convolutional-neural-networks
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Jan 2, 2018 - Python
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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Nov 7, 2019 - Python
Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018
computer-vision
deep-learning
pytorch
semi-supervised-learning
semantic-segmentation
adversarial-learning
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Jul 24, 2018 - Python
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
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May 13, 2020 - Python
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
machine-learning
natural-language-processing
computer-vision
deep-learning
neural-network
semi-supervised-learning
convolutional-networks
neural-machine-translation
representation-learning
gaussian-processes
variational-inference
kernel-learning
variational-autoencoders
deep-kernel-learning
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Jun 5, 2019
Learning to Cluster. A deep clustering strategy.
deep-neural-networks
deep-learning
clustering
artificial-intelligence
supervised-learning
semi-supervised-learning
artificial-neural-networks
transfer-learning
unsupervised-learning
loss-functions
similarity-metric
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Jan 8, 2020 - Python
Tensorflow implementation of Adversarial Autoencoders
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Jan 10, 2019 - Python
Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
tensorflow
semi-supervised-learning
convolutional-neural-networks
image-segmentation
gans
brain-mri
3d-gan
semi-supervised-gan
medical-image-analysis
few-shot-learning
unet-3d
unet-tensorflow
multi-modal-imaging
iseg-challenge
weightnorm-3d
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Oct 2, 2019 - Python
Triple-GAN: a unified framework for classification and class-conditional generation in semi-supervised learing
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Jan 24, 2018 - Python
Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)
deep-learning
semi-supervised-learning
autoencoders
link-prediction
graph-representation
node-classification
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Jul 18, 2019 - Python
Semi-supervised GAN in "Improved Techniques for Training GANs"
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Nov 27, 2019 - Python
Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge
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Aug 9, 2018 - Python
[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition
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Jan 28, 2020 - Python
Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)
computer-vision
deep-learning
semi-supervised-learning
weakly-supervised-learning
instance-segmentation
scene-parsing
eccv-2018
panoptic-segmentation
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Sep 20, 2018 - MATLAB
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
benchmark
deep-learning
protein-structure
language-modeling
pytorch
dataset
semi-supervised-learning
protein-sequences
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May 1, 2020 - Python
arnaudvl
opened
Oct 25, 2019
VOSK Speech Recognition Toolkit
multilingual
python
voice-recognition
speech-recognition
semi-supervised-learning
speech-to-text
lifelong-learning
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Jan 11, 2020 - C
Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)
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May 30, 2019 - Jupyter Notebook
machine-learning
natural-language-processing
computer-vision
deep-learning
generative-model
semi-supervised-learning
graph-neural-networks
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May 7, 2020
Implementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)
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Oct 16, 2019 - Python
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
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Mar 24, 2020 - Python
spark157
commented
Jul 30, 2019
Hello,
I'm trying to run the fluorescence task with fully pretrained model weights (ie. for both the unsupervised pretraining and weights for the supervised task as well). I have downloaded the pretrained UniRep model but I'm thinking this does not include the model weights for the supervised task (fluorescence). Are these available or am I confusing something?
In the README.md|Loading a M
PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch
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Apr 13, 2020 - Python
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Great paper!
Tensorflow documentation says the EMA variables are created with (trainable=False) and added to the GraphKeys.ALL_VARIABLES collection. Now as they are not trainable they wont have the gradient applied on them, i understand that. But, as they depend upon the current trainable variables of the graph, and hence so do the predictions of the teacher network; an additional gradient will