Inferencing Based on Unsupervised Learning of Disentangled Representations

7 Mar 2018 Tobias Hinz Stefan Wermter

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. We propose a framework that combines an encoder and a generator to learn disentangled representations which encode meaningful information about the data distribution without the need for any labels... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Unsupervised Image Classification MNIST Bidirectional InfoGAN Accuracy 96.61 # 3
Unsupervised MNIST MNIST Bidirectional InfoGAN Accuracy 96.61 # 4

Methods used in the Paper


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