Deep interpolation – denoising data by removing independent noise
Isn’t this just a specific application of an autoencoder though? Because random noise isn’t learnable, it gets filtered out if you teach the network to compress&#x2F;decompress a sample with itself as target.<p><a href="https:&#x2F;&#x2F;blog.keras.io&#x2F;building-autoencoders-in-keras.html" rel="nofollow">https:&#x2F;&#x2F;blog.keras.io&#x2F;building-autoencoders-in-keras.html</a><p>With that said, it’s amazing how effective this concept is for cleaning up scientific data. Many have also used variational autoencoders to take it one step further and also cluster data by latent space features. I’ve used it myself to uncover various groups of behaviors in time-series from other cellular processes.