Timeline for Is it possible to make multi-layer autoencoder learn to completely repeat input?
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Apr 11, 2018 at 13:51 | comment | added | Bloc97 | An autoencoder learns a representation of the dataset, not single input neurons. If an autoencoder repeats it's input perfectly we can assume it overfit somewhere. You can make the network overfit by giving it a very small dataset. Or you can make the latent vector bigger than the input vector. However, if the latent vector is smaller the network cannot reproduce it's inputs perfectly for every input possible, but it can for a subset of the input space, if that subset is small enough. | |
| Mar 25, 2017 at 10:05 | vote | accept | dk14 | ||
| Mar 25, 2017 at 9:03 | answer | added | Hugh Perkins | timeline score: 2 | |
| Mar 25, 2017 at 7:15 | history | edited | dk14 | CC BY-SA 3.0 |
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| Mar 25, 2017 at 7:09 | history | asked | dk14 | CC BY-SA 3.0 |