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High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
Hence, with the extensive experimental results, we have demonstrated that by harnessing the power of the high-fidelity audio generation, the proposed GAAE model can learn powerful representation from unlabelled dataset leveraging a fewer percentage of labelled data as supervision/guidance.
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data
However, the model can become redundant if it is intended for a specific task.
Disentangled Representation Learning with Information Maximizing Autoencoder
Learning disentangled representation from any unlabelled data is a non-trivial problem.
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention
In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image.
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