A message from our CEO about the future of Stack Overflow and Stack Exchange. Read now.

Questions tagged [autoencoders]

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

Filter by
Sorted by
Tagged with
1
vote
1answer
31 views

What class of problem is this?

If I have a lot of input output pairs as training data <float Xi, float Yi> and I have a parametrized approximation function (I know the function algorithm, ...
0
votes
0answers
11 views

What is the need for Auxiliary Decoder in the VAE-GAN?

The below image taken from Tim Sainberg's GitHub repo (https://github.com/timsainb) shows the structure of a VAE-GAN: My question is about the second row in the diagram. Random samples drawn from z ...
4
votes
0answers
28 views

Autoencoder produces repeated artifacts after convergence

As experiment, I have tried using an autoencoder to encode height data from the alps, however the decoded image is very pixellated after training for several hours as show in the image below. This ...
3
votes
1answer
29 views

How are small scale features represented in an Inverse Graphics Network (autoencoder)?

This post refers to Fig. 1 of a paper by Microsoft on their Deep Convolutional Inverse Graphics Network: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/kwkt_nips2015.pdf Having ...
1
vote
0answers
44 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 ...
1
vote
0answers
21 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 ...
4
votes
1answer
73 views

What are some new deep learning models for learning latent representation of data?

I know that autoencoders are one type of deep neural networks that can learn the latent representation of data. I guess there should be several other models like autoencoders. What are some new deep ...
2
votes
1answer
40 views

Why does the denoising autoencoder always returns the same output?

I am trying to implement a denoising autoencoder (DAE) to remove noise from 1024-point FFT spectra. I am using two types of spectra: (1) that contain a distinctive high amplitude spectral peak and (2) ...
5
votes
1answer
63 views

Concrete example of latent variables and observables plugged into the Bayes' rule

In the context of the variational auto-encoder, can someone give me a concrete example of the application of the Bayes' rule $$p_{\theta}(z|x)=\frac{p_{\theta}(x|z)p(z)}{p(x)}$$ for a given latent ...
2
votes
0answers
22 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 ...
0
votes
0answers
22 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
26 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
2answers
45 views

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 ...
0
votes
1answer
55 views

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 ...
2
votes
2answers
37 views

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 ...
0
votes
0answers
12 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 ...
4
votes
1answer
98 views

Is there a Continuous Conditional Variational Autoencoder?

The Conditional Variational Autoencoder (CVAE), introduced in the paper Learning Structured Output Representation using Deep Conditional Generative Models (2015), is an extension of Variational ...
1
vote
1answer
55 views

What is the best loss function for convolution neural network and autoencoder?

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 ...
2
votes
1answer
58 views

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 ...
0
votes
0answers
19 views

Not clear about CoordConv

I read the CoordConv paper and I am a bit confused about its implementation for a GAN/VAE. I understand how to add 2 more channels to an image and pass that to a conv net (and there are good online ...
0
votes
0answers
14 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
131 views

Dense bottleneck layer in Autoencoder

I would like to use the bottleneck layer of U-Net (last layer of the encoder) to calculate the similarity between two images. For that I have to somehow flatten the last layer of the encoder. In my ...
1
vote
2answers
214 views

Autoencoder for MobileNetV2

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 pretrained model for the classification of the ...
0
votes
0answers
27 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 ...
0
votes
0answers
38 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 $D$ (...
2
votes
1answer
57 views

Reconstruction Errors in Auto Encoders after Training

Autoencoders are used for unsupervised anomaly detection by at first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous, if the new data ...
2
votes
1answer
45 views

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 ...
1
vote
2answers
153 views

Why are VAE's useful?

I am not sure I understand what is the advantage of using a VAE's over a deterministic Auto Encoder? For example, assuming we have just 2 labels, a deterministic Auto Encoder will always map a given ...
0
votes
0answers
70 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
1answer
138 views

Autoencoder for color images in Keras backed by MXNet

I need to train an autoencoder in Keras with the JPG images I took myself. ...
0
votes
0answers
32 views

Has anyone succeeded to (intentionally) overfit the neural network with MNIST?

I am currently studying myself with a subject "representational(expressive power) of neural network" and trying to intentionally fully overfit the neural network which means that at least the model ...
3
votes
1answer
328 views

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 ...
3
votes
2answers
99 views

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 ...
9
votes
3answers
1k views

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 ...
6
votes
1answer
297 views

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?
2
votes
1answer
89 views

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 ...
2
votes
1answer
72 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 ...
4
votes
2answers
2k views

Why are Variational autoencoder's output is 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 ...
2
votes
1answer
1k views

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?
2
votes
0answers
34 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. ...
10
votes
1answer
3k views

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 ...
3
votes
1answer
92 views

Disentangled VAE doesn't reconstruct accurate grids

I am trying to implement the disentangled VAE model according to this link. I want to understand the architecture of this model in order to customize it later. As infrastructure, I have a linux kernel ...
4
votes
1answer
2k 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 ...
4
votes
0answers
420 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 ...
0
votes
1answer
45 views

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 ...
2
votes
1answer
34 views

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 ...
5
votes
2answers
248 views

Genetic programming in autoencoder

I am trying to understand how genetic programming can be used in autoencoders. I am going through a few papers (the classic one and another) but they dont help me to even grasp the concept of genetic ...
1
vote
1answer
131 views

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 ...
3
votes
3answers
373 views

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?