Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
46
questions with no upvoted or accepted answers
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0answers
42 views
Are there transformer-based architectures that can produce fixed-length vector encodings given arbitrary-length text documents?
BERT encodes a piece of text such that each token (usually words) in the input text map to a vector in the encoding of the text. However, this makes the length of the encoding vary as a function of ...
4
votes
1answer
477 views
Adding a dense layer after a conv2d layer in a convolutional autoencoder
I am trying to implement a convolutional autoencoder with a dense layer at the bottleneck do to some dimensional reduction. I have seen two approaches for this which arent particularly scalable. The ...
4
votes
0answers
547 views
Sparsity constraint in a deep autoencoder
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 ...
3
votes
0answers
24 views
How to quantify the amount of information lost by the decoder NN in an AE?
Is there a way to quantify the amount of information lost in the lossy part of an autoencoder where the original input is compressed to a representation with less degrees of freedom?
I was thinking ...
3
votes
0answers
35 views
Enforcing sparsity constraints that make use of spatial contiguity
I have a deep learning network that outputs grayscale image reconstructions. In addition to good reconstruction performance (measured through mean squared error or some other measure like psnr), I ...
3
votes
0answers
53 views
Looking for the proper algorithm to compress many lowres images of nearby locations
I have an optimization problem that I'm looking for the right algorithm to solve.
What I have: A large set of low-res 360 images that were taken on a regular grid within a certain area. each of these ...
3
votes
2answers
145 views
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 ...
2
votes
0answers
24 views
Literature on the advantages of using an auto-encoder for classification
Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning):
Deploy directly a ...
2
votes
0answers
27 views
Compressing Parameters of an Response System
I have an input-output system, which is fully determined by 256 parameters, of which I know a significant amount are of less importance to the input-output pattern.
The data I have is some (64k in ...
2
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0answers
25 views
how to handle highly imbalanced multilabel classification?
I am working on a multilabel classification in which I am having 206 labels. When I saw the percentage of the number of 1's in each label they are way less than 0.1% for each label. The maximum ...
2
votes
1answer
43 views
Are Autoencoders for noise-reduction only suited to deal with salt-and-pepper kind of noise?
I'm currently looking at NN to deal with noisy data. I like the Autoencoder approach https://medium.com/@aliaksei.mikhailiuk/unsupervised-learning-for-data-interpolation-e259cf5dc957 because it seems ...
2
votes
0answers
104 views
Is it possible to use entity embedding with autoencoder for anomaly detetction?
I'm trying to build autoencoder in keras in order to detect anomalies. However, most of the data is categorical and I have to encode it. When it comes to production, categorical features can take new ...
2
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0answers
245 views
How does deepfake technology work with multiple people in a single frame?
I was watching this video from corridor crew, according to them, they have used deepfake technology to create this video. I myself have never made a deepfake videos, but I have enough knowledge in the ...
2
votes
0answers
97 views
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 ...
2
votes
1answer
94 views
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 ...
2
votes
0answers
38 views
Autoencoder why it is special for image decoding?
I have read about auto encoder. Understood what is encoding part, and decoding part, and the latent space. Now, i tried to implement this in keras. Below is the code.
...
1
vote
0answers
16 views
Dealing with bias in multi-channel auto encoders
The problem
I have a multi-channel 1D signal I want to auto-encode.
I am unable to resonstruct the input when the number of channels increases.
Code
I am using a convolutional encoder, and a ...
1
vote
0answers
17 views
Underfitting a single batch: Can't cause autoencoder to overfit multi-sample batches of 1d data. How to debug?
TL;DR
I am unable to overfit batches with multiple samples using autoencoder.
Fully connected decoder seems to handle more samples per batch than conv decoder, but then also fails when number of ...
1
vote
0answers
21 views
variational auto encoder loss goes down but does not reconstruct input. out of debugging ideas
My variational autoencoder seems to work for MNIST, but fails on slightly "harder" data.
By "fails" I mean there are at least two apparent problems:
Very poor reconstruction, for ...
1
vote
0answers
34 views
VAE giving near zero output when latent space dimension is large
I'm training a VAE to reconstruct some input (channels picked up by some MIMO BS for context) and I ran an experiment on the training set to see how the performance improves with the latent space ...
1
vote
0answers
22 views
How can I improve the performance on unseen data for semantic segmentation using an auto-encoder?
I am using simple autoencoders for the task of semantic segmentation on the VOC2012 dataset. I am currently using a simple autoencoder based model. It is trained on adam optimizer with cross-entropy ...
1
vote
0answers
19 views
Why do we add additional axis in CNN autoencoder while denoising?
I am currently learning about autoencoders and I follow https://www.tensorflow.org/tutorials/generative/autoencoder
When denoising images, authors of tutorial add an additional axis to the data and I ...
1
vote
0answers
19 views
Role of autoencoder in Hierarchical Extreme Learning Machine
I want to build HELM neural network that consists of autoencoder (AE) and one class classification (OC).
HELM with AE and OC have following shape:
That is, hidden layer output of AE is input of OC.
...
1
vote
0answers
77 views
How can a de-noising auto-encoder act as an anomaly detection model?
In some research papers, I have seen that, for training the autoencoders, instead of giving the non-anomalous input images, they add some anomalies to the normal input images, and train the auto-...
1
vote
0answers
31 views
How much data do we need for making a successful de-noising auto-encoder?
Is there a guide how much data do you need for making successful denoising model using autoencoders?
Or the rule is, the more data, the better it is?
I tried with small dataset 350 samples, to see ...
1
vote
0answers
47 views
Does the reduction of the dimensions over multiple layers allow more details to be stored within the final representation?
From : https://debuggercafe.com/implementing-deep-autoencoder-in-pytorch/
the following autoencoder is defined
...
1
vote
0answers
38 views
How estimate the minimum size of an autoencoder to overfit the training data?
Given e.g. $1$M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$, how to estimate the minimum network size required ...
1
vote
0answers
26 views
Can denoising auto-encoders be convolutional and fully connected?
I have been reading lately on autoencoders a lot. I just wanted to summarize my understanding of denoising autoencoders. As far as I understand they can be
Fully connected (in which case, they will ...
1
vote
2answers
64 views
How can I use autoencoders to analyze patterns and classify them?
I generated a bunch of simulation data from a complex physical simulation that spits out patterns. I am trying to apply unsupervised learning to analyze the patterns and ideally classify them into ...
1
vote
0answers
55 views
Why is my variational auto-encoder generating random noise?
This is my first variational autoencoder. Background info: I am using the MNIST digits dataset. The model is created and trained in PyTorch. The model is able to get a reasonably low loss, but the ...
1
vote
0answers
29 views
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 ...
1
vote
0answers
14 views
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 ...
1
vote
0answers
17 views
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-...
1
vote
0answers
40 views
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 $D$ (...
1
vote
0answers
83 views
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 ...
0
votes
0answers
14 views
Can an Autoencoder be used to create a simple text-completion plugin (code-completion for programming)?
Can an Autoencoder neural network be used to create a simple code completion plugin for a developer using a certain programming language ?
The idea is that the training data will be generated from ...
0
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0answers
18 views
Is the char-CNN-RNN text encoder is an encoder part of an auto-encoder?
The char-CNN-RNN encoder is relatively a popular encoder. It was proposed in this paper.
Is it an encoder part of an autoencoder? Or an independent neural network?
0
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0answers
44 views
How to compare multiple one-class variational autoencoders?
I have trained multiple one-class vanilla variational autoencoders that each learn the distribution of one class and have the same architecture. The classes are mostly discrete, but there are several ...
0
votes
0answers
35 views
Which models can I use for supervised learning with images?
I have to do a project that detects fabric surface errors and I will use machine learning methods to deal with it. I have a dataset that includes around six thousand fabric surface images with the ...
0
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0answers
29 views
How did they use their dataset with VAEs?
Old Photo Restoration via Deep Latent Space Translation (https://paperswithcode.com/paper/old-photo-restoration-via-deep-latent-space)
In the article, it says : "We propose to restore old photos ...
0
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0answers
28 views
Deep Continuous Clustering algorithm - just one output cluster
I use the DCC algorithm to cluster some data.
The whole algorithm is available here, but shortly it is:
construct mkNN graph of the data points (the connected components of it are the clusters).
...
0
votes
0answers
50 views
What is the time complexity for training a single-hidden layer auto-encoder?
What is the time complexity for training a single-hidden layer auto-encoder, for 1 epoch?
You can assume that there are $n$ training examples, $m$ features, and $k$ neurons in the hidden layer, and ...
0
votes
1answer
123 views
What is the best activation function for the embedding layer in a deep auto-encoder?
I am designing a deep autoencoder for graph embedding (exactly node embedding) following this paper SDNE. In the original paper, they used the sigmoid activation for all hidden layers in the ...
0
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0answers
33 views
How to convert something to vectors
I wanted to create an encoder, which is the first part of an autoencoder. I do not want to build the whole autoencoder but rather wanted to test whether my mobile device can support running an encoder ...
0
votes
0answers
29 views
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 ...
-1
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0answers
14 views
sequential autoencoder: RNN decoding comparing to RNN with attention decoding
I've implemented an RNN-ecnoder-decoder model and the same model with attention mechanism.
That is the RNN model:
and that is the attentioned model:
then I asked for a theoretical question:
The ...
