Unsupervised MNIST Edit Task
Methodology • Unsupervised Representation Learning
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Accuracy on MNIST when training without any labels
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| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
NeurIPS 2016
• tensorflow/models
• 
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
Ranked #2 on
Image Generation
on Stanford Dogs
IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Adversarial Autoencoders
• eriklindernoren/PyTorch-GAN
• 
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
Ranked #4 on
Unsupervised Image Classification
on MNIST
DATA VISUALIZATION DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST VARIATIONAL INFERENCE
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
Ranked #1 on
Unsupervised Semantic Segmentation
on COCO-Stuff-3
IMAGE CLUSTERING SEMANTIC SEGMENTATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Stacked Capsule Autoencoders
NeurIPS 2019
• phanideepgampa/stacked-capsule-networks
•
In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.
Ranked #2 on
Unsupervised MNIST
on MNIST
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model.
Ranked #5 on
Unsupervised Image Classification
on MNIST
ROBUST CLASSIFICATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Inferencing Based on Unsupervised Learning of Disentangled Representations
• tohinz/Bidirectional-InfoGAN
•
Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.
Ranked #3 on
Unsupervised Image Classification
on MNIST
Improving Self-Organizing Maps with Unsupervised Feature Extraction
We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning.
Ranked #3 on
Unsupervised MNIST
on MNIST
PixelGAN Autoencoders
In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Ranked #7 on
Unsupervised Image Classification
on MNIST