Questions tagged [autoencoders]

For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.

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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 ...
HelloGoodbye's user avatar
8 votes
1 answer
4k views

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 ...
Glrs's user avatar
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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 ...
MattSt's user avatar
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Why VQ-VAE instead of VAE?

From the paper on VQ-VAE, it said that the vector quantized variational autoencoder (VQ-VAE), differs from VAEs in two key ways: the encoder network output discrete, rather than continuous, codecs ...
Nervous Hero's user avatar
3 votes
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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 ...
0x90's user avatar
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0 answers
64 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 ...
Jane Sully's user avatar
3 votes
0 answers
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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 ...
matthias_buehlmann's user avatar
3 votes
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1k 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 ...
Eka's user avatar
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3 votes
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475 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 ...
Bill's user avatar
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2 votes
1 answer
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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 ...
tyassine's user avatar
2 votes
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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 ...
Tommaso Bendinelli's user avatar
2 votes
0 answers
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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 ...
t-smart's user avatar
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2 votes
2 answers
530 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 ...
Pavan Inguva's user avatar
2 votes
0 answers
240 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 ...
jdw136's user avatar
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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 ...
m2rik's user avatar
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Is it reasonable to ask for the same time-regularity of the high and low dimensional signals?

Consider we are dealing with sequential data sampled from a continuous time signal $x(t)\in \mathbb{R}^n$, so that the dataset will look like $\{x_0,x_1,…,x_n\}$, with $x_i= x(t_i)$. Assume that we ...
user8354084's user avatar
1 vote
0 answers
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Replicating conv autoencoder for anomaly detection, very blurry reconstructions

I’m trying to train an autoencoder on the hazelnut dataset of MVTec AD for reconstruction to detect anomalies. I’m am trying to replicate the results of this study: https://arxiv.org/pdf/2008.12977....
JeanMi's user avatar
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Latent Diffusion Model Can't Learn the Latent Space of a VAE for the MNIST-Fashion Dataset

I'm currently playing around with LDMs on the MNIST-Fashion dataset. I thought the VQVAEs used in the original paper were a bit overkill for what I'm doing (and I don't fully understand how they ...
sb3's user avatar
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1 vote
0 answers
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Pseudo Label Generation for Generative Cooperative Learning

I am trying to implement this paper for unsupervised video anomaly detection. The gist of the paper seems to be: Create a dataset for an unsupervised setting, by mixing up the train and anomalous ...
satan 29's user avatar
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Is it possible for PixelCNN to tell us what it generates?

I coded PixelCNN with the help of Keras official website. Also, I read the paper. I can use PixelCNN, similar to a decoder or generator (to generate samples). My question is, "is it possible to ...
Pouyan's user avatar
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Is there a name for this model?

I have an image autoencoder model trained as follows: Step 1) train a GAN to obtain a generator capable of drawing from the data manifold by sampling a normal distribution in latent space Step 2) ...
user11305730's user avatar
1 vote
1 answer
805 views

What is the state of the art concerning autoencoder that connect 2 images that are not similar but are physicaly related?

I am currently working on an autoencoder that connect two images. The first one can be seen as the electron flow and the second one is the electrostatic potential seen by the electrons. Long story ...
Gaetan Percebois's user avatar
1 vote
0 answers
51 views

What is the conceptual difference between convolutional neural networks and auto-encoders?

I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter: I wonder how it is different from Auto-Encoders: For me, it ...
Ben's user avatar
  • 205
1 vote
0 answers
43 views

How do I select the number of neurons for each layer in an auto-encoder for dimensionality reduction?

I am trying to apply an auto-encoder for dimensionality reduction. I wonder how it will be applied on a large dataset. I have tried this code below. I have total of 8 features in my data and I want to ...
user12's user avatar
  • 111
1 vote
0 answers
29 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 ...
Gulzar's user avatar
  • 759
1 vote
0 answers
92 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 ...
Gulzar's user avatar
  • 759
1 vote
0 answers
322 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 ...
Gulzar's user avatar
  • 759
1 vote
0 answers
80 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 ...
Ravi Teja's user avatar
1 vote
0 answers
122 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. ...
Zekhire's user avatar
  • 11
1 vote
0 answers
83 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-...
ans_ak's user avatar
  • 11
1 vote
0 answers
81 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 ...
Vesko Vujovic's user avatar
1 vote
0 answers
73 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 ...
blue-sky's user avatar
  • 335
1 vote
0 answers
72 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 ...
ENECO's user avatar
  • 21
1 vote
0 answers
32 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 ...
PagMax's user avatar
  • 111
1 vote
0 answers
32 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 ...
hechth's user avatar
  • 11
1 vote
0 answers
18 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 ...
Ijlal's user avatar
  • 11
1 vote
0 answers
20 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-...
Alex Marshall's user avatar
1 vote
0 answers
42 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 $D$ (...
fazega's user avatar
  • 111
1 vote
0 answers
100 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 ...
Tamilarasu Ulaganathan's user avatar
0 votes
0 answers
18 views

Does the fixed context in attention mechanism is accquired after getting the decoder hidden layer of the first hidden state?

here, the fixed context vector (ci) is used for the decoder model, why the decoder model also used by the attention weights. On the first (c1), does that mean the decoder does not have context ? (i = ...
Jeremy Kenn's user avatar
0 votes
0 answers
26 views

Is there a better way for my CNN to handle random values?

I made an autoencoder to, ideally, turn an image into seemingly random numbers(Using a loss that determines randomness) and turn those random numbers into the original image. The results were kind of ...
Nathanael Suarez's user avatar
0 votes
0 answers
19 views

Is synthetic data just a placebo for immature models?

I apologize for the provocative question, but let me elaborate. I am trying to wrap my head around the logic of synthetic data. When you train a model what you are trying to do is to teach the ground ...
Pigna's user avatar
  • 295
0 votes
0 answers
24 views

Seeking methods to incorporate arbitrary actuator faults for Control Optimization

I am working on a problem where a control method, backed by a Neural Network (NN), dictates the movement of a 1D actuator to influence a specific process. This actuator can move linearly within a set ...
IsolatedSushi's user avatar
0 votes
0 answers
7 views

autoencoders for anomaly detection, training individual models for different users or roles, how?

Do I first train a generic model for all of my users on a network, say for a network anomaly detection example, then fine tune for each user on their own subset of the training data? But I'd be using ...
mLstudent33's user avatar
0 votes
0 answers
62 views

Why does each row of data have the same bottleneck features in the Autoencoder after training?

I was training an autoencoder for anomaly detection and I wish to extract the bottleneck features of the encoder for K-NN. The model architecture is as such: ...
Aengus's user avatar
  • 1
0 votes
0 answers
108 views

How do i approach creating a masked auto-encoder for feature extraction

I trained Masked Autoencoder-based models in order to use the encoder as a backbone for another task. Pretraining has been done in a Self-Supervised manner on image data. Now that it comes to my ...
Mitch's user avatar
  • 1
0 votes
0 answers
19 views

Analysis of the output samples from an autoencoder

I am conducting some experiments on an autoencoder as part of my research project. For our first experiment, we have a feedforward neural network (using pytorch), which is being given an input of ...
Darth_Vader's user avatar
0 votes
0 answers
50 views

Is this a valid application of Autoencodeers/VAE?

I am trying to predict a spectrum (1D vector) from various scalar inputs which are known to be correlated. As the spectrum vector is very long (4000 points) it was suggested that I use dimensionality ...
Christopher McQueen's user avatar
0 votes
0 answers
10 views

What would be the best approach to resume longer texts in one word?

I am trying to create a model capable of resuming longer texts (my dataset has up to 140 words for each instance) in a single word (or multiple separate words). The idea is to synthetize positive or ...
Victor Ferreira's user avatar
0 votes
0 answers
24 views

How do I make an autoencoder and make it work on extracting the feature of a stationary wave?

I have a project to complete in a day, and I know that doing it in a day is a bit far-fetched. The problem is this - "Design an autoencoder with two neurons as the constriction, multiple hidden ...
Neeladri Reddy's user avatar