Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
41
questions
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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
21 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 ...
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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
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0answers
24 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
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1answer
54 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
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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
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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 ...
3
votes
1answer
52 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
51 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
54 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
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0answers
17 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 ...
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0answers
13 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-...
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0answers
88 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
161 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 ...
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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 ...
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0answers
37 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
52 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
128 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 ...
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0answers
67 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
106 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.
...
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0answers
31 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
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1answer
227 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
97 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 ...
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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 ...
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votes
1answer
262 views
How to choose the dimensions of the encoding layer in autoencoders?
how to choose the dimensions of the encoding layer in autoencoders?please explain what should be the dimensions of the encoding and decoding layers
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0answers
58 views
AUTOENCODERS FOR CREDIT CARD FRUD DETECTION
Am working on credit card fraud detection problem using autoencoders. Regarding that I have some doubts given below :
The dataset for the above problem has been downloaded from kaggle which is highly ...
2
votes
1answer
58 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
1k 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 ...
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1answer
903 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?
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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
89 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
Batch Normalization in Deep Autoencoders?
Does it make sense to use Batch Normalization in Deep (stacked) or/and Sparse Autoencoders? I cannot find any resources for that, so is it safe to assume that since it works for other DNNs it will ...
4
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0answers
397 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
44 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
225 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
124 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
334 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?
