Using Autoencoders to Clean (or Denoise) Noisy Images with the help of Fashion MNIST | Unsupervised Deep Learning with TensorFlow

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Figure 1. Before and After the Noise Reduction of an Image of a Playful Dog (Photo by Anna Dudkova on Unsplash)

If you are on this page, you are also probably somewhat familiar with different neural network architectures. You have probably heard of feedforward neural networks, CNNs, RNNs and that these neural networks are very good for solving supervised learning tasks such as regression and classification.

But, we have a whole world of problems on the unsupervised learning sphere such as dimensionality reduction, feature extraction, anomaly detection, data generation, and augmentation as well as noise reduction. For these tasks, we need the help of special neural networks that are developed particularly for unsupervised learning tasks. Therefore, they must be able to solve mathematical equations without needing supervision. One of these special neural network architectures is autoencoders.

What are autoencoders?

Autoencoders are neural network architectures that consist of two sub-networks, namely, encoder and decoder networks, which are tied to each other with a latent space. Autoencoders were first developed by Geoffrey Hinton, one of the most respected scientists in the AI community, and the PDP group in the 1980s. Hinton and the PDP Group aimed to address the “backpropagation without a teacher” problem, a.k.a. unsupervised learning, by using the input as the teacher. In other words, they simply used feature data both as feature data and label data. Let’s take a closer look at how autoencoders work!

#deep-learning #data-science #machine-learning #tensorflow #python