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. While current approaches focus mostly on the generative aspects of GANs, our framework can be used to perform inference on both real and generated data points. Experiments on several data sets show that the encoder learns interpretable, disentangled representations which encode descriptive properties and can be used to sample images that exhibit specific characteristics.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Unsupervised Image Classification MNIST Bidirectional InfoGAN Accuracy 96.61 # 4
Unsupervised MNIST MNIST Bidirectional InfoGAN Accuracy 96.61 # 6


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