Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
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Reduce same sample distance in VAE encodings
I'm working on a beta VAE model learning a latent representation used as a similarity metric for image registration.
One of the main problems I'm facing is that the encoder + sampler output doesn't ...
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Are there any general tips for troubleshooting a VAE when apparently it is not learning?
I am trying to train a VAE for anomaly detection. I chose one architecture from this Github repository and I adjusted the input and output to match what I need. In my case, the input (and hence the ...
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Using ML to encypher data for production
I am looking for research and experience working with ML models to ingest data for tasks, like text analysis, and creates a system that copies (or in other words enciphers) the input data, to then ...
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What can I do with an autoencoder? [duplicate]
I cannot find information in detail about autoencoder
What can I do with an autoencoder (and how can I do this), practically speaking?
What does the encoder (this part I think I understand) and a ...
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Dealing with empty frames in MRI images
I started working on the application of deep learning in medical imaging recently. While dealing with MRI images in the BraTS dataset, I observe that first and last few frames are always completely ...
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Camera pose to environment Mapping
I would like to teach a model the environment of a room. I'm doing so by mapping a camera pose (x, y, z, q0, q1, q2, q3) to its corresponding image; where x, y, z represent location in Cartesian ...
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Is there a continuous conditional variational auto-encoder?
The Conditional Variational Autoencoder (CVAE), introduced in the paper Learning Structured Output Representation using Deep Conditional Generative Models (2015), is an extension of Variational ...
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What is the best loss function for convolution neural network and autoencoder? [closed]
What is the best choice for loss function in Convolution Neural Network and in Autoencoder in particular - and why?
I understand that the MSE is probably not the best choice, because little ...
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How should I detect an object in a camera image?
I would like to create a model, that will tell me if one type of object is in an image or not.
So, for example, I have a camera and I would like to see when one object gets into the shot.
Object ...
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Limits for a bottleneck
I have some 64x64 pixels frames from a (simulated) video, with a spaceship moving on a fixed background. The spaceship moves in a straight line with constant velocity from left to right (along the x-...
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If I use MobileNetV2 for the encoder, can I use a different architecture for the decoder?
I have way more unlabeled data than labeled data. Therefore I would like to train an autoencoder using MobileNetV2 as the encoder. Then I will use the pre-trained model for the classification of the ...
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Deep Generative Networks Probability of "Success"
I have built various "successful" GANs or VAEs that can generate realistic images reliably, but in either case the generative step is sampling a latent feature vector from some distribution and ...
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How to learn to sample?
Imagine you have access to a dataset of pairs $(s, \hat{\pi}(s))$ where s is a state in a high dimension continuous space $S$, $\pi(s)$ is a probabilistic distribution on a large discrete space $D$ (...
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How can auto-encoders compute the reconstruction error for the new data?
Autoencoders are used for unsupervised anomaly detection by first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous if the new ...
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Is it possible for a neural network to be used to compress data?
When training a neural network, we often run into the issue of overfitting.
However, is it possible to put overfitting to use? Basically, my idea is, instead of storing a large dataset in a database, ...
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How to add some data input in a CNN?
There is this problem I have encountered, I was trying to classify the pixels from input image into classes, sort of like segmentation, using a encoder-decoder CNN. The “interested” pixels usually ...
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What is the advantage of using a VAE over a deterministic auto-encoder?
What is the advantage of using a VAE over a deterministic auto-encoder?
For example, assuming we have just 2 labels, a deterministic auto-encoder will always map a given image to the same latent ...
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Can we use Autoencoders for unsupervised CNN feature learning?
I searched through the internet but couldn't find a reliable article that answers this question.
Can we use Autoencoders for unsupervised CNN feature learning of unlabeled images like the below
and ...
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Variational Autoencoder task for better feature extraction
I have a CNN with the regression task of a single scalar.
I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with ...
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Do we also need to model a probability distribution for the decoder of a VAE?
I'm working on understanding VAEs, mostly through video lectures of Stanford cs231n, in particular lecture 13 tackles on this topic and I think I have a good theoretical grasp.
However, when looking ...
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What are the purposes of autoencoders?
Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...
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How should we choose the dimensions of the encoding layer in auto-encoders?
How should we choose the dimensions of the encoding layer in auto-encoders?
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Why isn't the Credit Card Fraud Detection dataset from Kaggle already balanced?
I am working on a credit card fraud detection problem using autoencoders. I have a question regarding the dataset I'll be using.
I've downloaded the dataset for the above problem from Kaggle, which ...
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What are some limitations of using Collaborative Deep learning for Recommender systems?
Recently I worked on a paper by Hao Wang, Collaborative Deep learning for Recommender Systems; which uses a two way tightly coupled method, Collaborative filtering for Item correlation and Stacked ...
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Why is the variational auto-encoder's output blurred, while GANs output is crisp and has sharp edges?
I observed in several papers that the variational autoencoder's output is blurred, while GANs output is crisp and has sharp edges.
Can someone please give some intuition why that is the case? I did ...
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Why doesn't VAE suffer mode collapse?
Mode collapse is a common problem faced by GANs. I am curious why doesn't VAE suffer mode collapse?
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Loss jumps abruptly when I decay the learning rate with Adam optimizer in PyTorch
I'm training an auto-encoder network with Adam optimizer (with amsgrad=True) and ...
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Does it make sense to use batch normalization in deep (stacked) or sparse auto-encoders?
Does it make sense to use batch normalization in deep (stacked) or sparse auto-encoders?
I cannot find any resources for that. Is it safe to assume that, since it works for other DNNs, it will also ...
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Is there any way and any reason why one would introduce a sparsity constraint on a deep auto-encoder?
Is there any way and any reason why one would introduce a sparsity constraint on a deep autoencoder?
In particular, in deep autoencoders, the first layer often has more units than the dimensionality ...
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What are good parameters of an encoder?
I am trying to assess an encoder in my autoencoder. I can not seem to grasp which specs make an encoder better than other one in, lets say, unsupervised learning. For example, I am trying to teach my ...
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Do Le et al. (2012) train all three autoencoder layers at a time, or just one?
Le et al. 2012 use a network of 1 billion parameters to learn neurons that respond to faces, cats, pedestrians, etc. without labels (unsupervised).
Their network is built with three autoregressive ...
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How can genetic programming be used in the context of auto-encoders?
I am trying to understand how genetic programming can be used in the context of auto-encoders. Currently, I am going through 2 papers
Training Feedforward Neural Networks Using Genetic Algorithms (a ...
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Equilateral and One-of-n encoding
I was reading AI For Humans Vol. 1 by Jeff Heaton when I came across the terms "equilateral encoding" and "one-of-n encoding." The explanations unfortunately made no sense to me and the reddit threads ...
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What is the difference between encoders and auto-encoders?
How are the layers in a encoder connected across the network for normal encoders and auto-encoders? In general, what is the difference between encoders and auto-encoders?
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Can autoencoders be used for supervised learning?
Can autoencoders be used for supervised learning without adding an output layer? Can we simply feed it with a concatenated input-output vector for training, and reconstruct the output part from the ...
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