How does encoder decoder network works?Tips and tricks for designing time-series variational autoencodersHow to generate image using deep learningMultilabel image classification: is it necessary to have traning data for each combination of labels?Unsupervised Anomaly Detection in Imageswhy is mse training drastically different from the begining of each training with Encoder-Decoder
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How does encoder decoder network works?
Tips and tricks for designing time-series variational autoencodersHow to generate image using deep learningMultilabel image classification: is it necessary to have traning data for each combination of labels?Unsupervised Anomaly Detection in Imageswhy is mse training drastically different from the begining of each training with Encoder-Decoder
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
Let's say I trained an encoder-decoder network on cat dataset using reconstruction error as loss function. The network is fully trained and the decoder is able to reconstruct good cat images.
Now what if I use the same network and input a dog image. Whether the network be able to reconstruct dog image ?
neural-network deep-learning autoencoder
$endgroup$
add a comment |
$begingroup$
Let's say I trained an encoder-decoder network on cat dataset using reconstruction error as loss function. The network is fully trained and the decoder is able to reconstruct good cat images.
Now what if I use the same network and input a dog image. Whether the network be able to reconstruct dog image ?
neural-network deep-learning autoencoder
$endgroup$
add a comment |
$begingroup$
Let's say I trained an encoder-decoder network on cat dataset using reconstruction error as loss function. The network is fully trained and the decoder is able to reconstruct good cat images.
Now what if I use the same network and input a dog image. Whether the network be able to reconstruct dog image ?
neural-network deep-learning autoencoder
$endgroup$
Let's say I trained an encoder-decoder network on cat dataset using reconstruction error as loss function. The network is fully trained and the decoder is able to reconstruct good cat images.
Now what if I use the same network and input a dog image. Whether the network be able to reconstruct dog image ?
neural-network deep-learning autoencoder
neural-network deep-learning autoencoder
asked 9 hours ago
ashukidashukid
39211 bronze badges
39211 bronze badges
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$begingroup$
It probably won't. The whole point of the training was to encode cat images and thus the network has tried to learn what information is the most necessary to keep to ensure a low reconstruction error (i.e. what separates one cat from another) and what information can it throw away (i.e. what characteristics appear in all cat images and can be discarded).
That being said, a dog image would produce a fairly decent reconstruction because most features are shared between both animals. If you try, however, to reconstruct something completely different (e.g. a car) then it would probably fail.
$endgroup$
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$begingroup$
It probably won't. The whole point of the training was to encode cat images and thus the network has tried to learn what information is the most necessary to keep to ensure a low reconstruction error (i.e. what separates one cat from another) and what information can it throw away (i.e. what characteristics appear in all cat images and can be discarded).
That being said, a dog image would produce a fairly decent reconstruction because most features are shared between both animals. If you try, however, to reconstruct something completely different (e.g. a car) then it would probably fail.
$endgroup$
add a comment |
$begingroup$
It probably won't. The whole point of the training was to encode cat images and thus the network has tried to learn what information is the most necessary to keep to ensure a low reconstruction error (i.e. what separates one cat from another) and what information can it throw away (i.e. what characteristics appear in all cat images and can be discarded).
That being said, a dog image would produce a fairly decent reconstruction because most features are shared between both animals. If you try, however, to reconstruct something completely different (e.g. a car) then it would probably fail.
$endgroup$
add a comment |
$begingroup$
It probably won't. The whole point of the training was to encode cat images and thus the network has tried to learn what information is the most necessary to keep to ensure a low reconstruction error (i.e. what separates one cat from another) and what information can it throw away (i.e. what characteristics appear in all cat images and can be discarded).
That being said, a dog image would produce a fairly decent reconstruction because most features are shared between both animals. If you try, however, to reconstruct something completely different (e.g. a car) then it would probably fail.
$endgroup$
It probably won't. The whole point of the training was to encode cat images and thus the network has tried to learn what information is the most necessary to keep to ensure a low reconstruction error (i.e. what separates one cat from another) and what information can it throw away (i.e. what characteristics appear in all cat images and can be discarded).
That being said, a dog image would produce a fairly decent reconstruction because most features are shared between both animals. If you try, however, to reconstruct something completely different (e.g. a car) then it would probably fail.
answered 1 hour ago
TmBrdyTmBrdy
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