I have a question about variational autoencoders (VAE),
I need to generate new data from my dataset which contains just numerical data, so i want to use VAE for that task, but all the available tutorials and articles use images as input data for the variatioanl autoencoder.
My question is: can i use VAE for generating new data from my datasets eventhough my data is not images ??
Thank you.
Short answer is yes. You should read up a bit on the basics of neural nets if this wasn't obvious already - an image is just a Channel X Height X Width dimensional vector. You might use different kinds of layers in your network to suit the kind of data that you have to give a better inductive bias, but otherwise nothing changes. Follow those tutorials!
Related
Recently I was going through the paper : "Intriguing Properties of Contrastive Losses"(https://arxiv.org/abs/2011.02803). In the paper(section 3.2) the authors try to determine how well the SimCLR framework has allowed the ResNet50 Model to learn good quality/generalised features that exhibit hierarchical properties. To achieve this, they make use of K-means on intermediate features of the ResNet50 model (intermediate means o/p of block 2,3,4..) & quote the reason -> "If the model learns good representations then regions of similar objects should be grouped together".
Final Results :
KMeans feature visualisation
I am trying to replicate the same procedure but with a different model (like VggNet, Xception), are there any resources explaining how to perform such visualisations ?
The procedure would be as follow:
Let us assume that you want to visualize the 8th layer from VGG. This layer's output might have the shape (64, 64, 256) (I just took some random numbers, this does not correspond to actual VGG). This means that you have 4096 256-dimensional vectors (for one specific image). Now you can apply K-Means on these vectors (for example with 5 clusters) and then color your image corresponding to the clustering result. The coloring is easy, since the 64x64 feature map represents a scaled down version of your image, and thus you just color the corresponding image region for each of these vectors.
I don't know if it might be a good idea to do the K-Means clustering on the combined output of many images, theoretically doing it on many images and one a single one should both give good results (even though for many images you probably would increase the number of clusters to account for the higher variation in your feature vectors).
I am new in deep learning field, i would like to ask about unlabeled dataset for Anomaly Detection using Autoencoder. my confusing part start at a few questions below:
1) some post are saying separated anomaly and non-anomaly (assume is labelled) from the original dataset, and train AE with the only non-anomaly dataset (usually amount of non-anomaly will be more dominant). So, the question is how am I gonna separate my dataset if it is unlabeled?
2) if I train using the original unlabeled dataset, how to detect the anomaly data?
Label of data doesn't go into autoencoder.
Auto Encoder consists of two parts
Encoder and Decoder
Encoder: It encodes the input data, say a sample with 784 features to 50 features
Decoder: from those 50 features it converts it back to original feature i.e 784 features.
Now to detect anomaly,
if you pass an unknown sample, it should be converted back to its original sample without much loss.
But if there is a lot of error in converting it back. then it could be an anomaly.
Picture Credit: towardsdatascience.com
I think you answered the question already yourself in part: The definition of an anomaly is that it should be considered "a rare event". So even if you don't know the labels, your training data will contain only very few such samples and predominantly learn on what the data usually looks like. So both during training as well as at prediction time, your error will be large for an anomaly. But since such examples should come up only very seldom, this will not influence your embedding much.
In the end, if you can really justify that the anomaly you are checking for is rare, you might not need much pre-processing or labelling. If it occurs more often (a threshold is hard to give for that, but I'd say it should be <<1%), your AE might pick up on that signal and you would really have to get the labels in order to split the data... . But then again: This would not be an anomaly any more, right? Then you could go ahead and train a (balanced) classifier with this data.
I'm implementing LDA using Java. I know how the algorithm works. In the end of the training (the given iterations) I will get 2 matrices (topic-word and document-topic) that represent the set of the input documents.
My problem is that when I input a new document (query) I want to use these matrices (or any other way) to get the document-topic vector of that query. How would I do that?
Are you using Variational Inference or Gibbs Sampling?
For Gibbs Sampling a typical approach is adding the new document/s to the inference, and only updating its own counters, keeping constant the counters for the documents you used to learn the model.
This is specified in equations 84 and 85 in Parameter Estimation for Text Analysis
I guess there has to be a similar approach in VI LDA.
I am currently looking into multi-labeling classification and I have some questions (and I couldn't find clear answers).
For the sake of clarity let's take an example : I want to classify images of vehicles (car, bus, truck, ...) and their make (Audi, Volkswagen, Ferrari, ...).
So I thought about training two independant CNN (one for the "type" classification and one fore the "make" classifiaction) but I thought it might be possible to train only one CNN on all the classes.
I read that people tend to use sigmoid function instead of softmax to do that. I understand that sigmoid does not sum up to 1 like softmax does but I dont understand in what doing that enables to do multi-labeling classification ?
My second question is : Is it possible to take into account that some classes are completly independant ?
Thridly, in term of performances (accuracy and time to give the classification for a new image), isn't training two independant better ?
Thank you for those who could give my some answers or some ideas :)
Softmax is a special output function; it forces the output vector to have a single large value. Now, training neural networks works by calculating an output vector, comparing that to a target vector, and back-propagating the error. There's no reason to restrict your target vector to a single large value, and for multi-labeling you'd use a 1.0 target for every label that applies. But in that case, using a softmax for the output layer will cause unintended differences between output and target, differences that are then back-propagated.
For the second part: you define the target vectors; you can encode any sort of dependency you like there.
Finally, no - a combined network performs better than the two halves would do independently. You'd only run two networks in parallel when there's a difference in network layout, e.g. a regular NN and CNN in parallel might be viable.
In the below code, they use autoencoder as supervised clustering or classification because they have data labels.
http://amunategui.github.io/anomaly-detection-h2o/
But, can I use autoencoder to cluster data if I did not have its labels.?
Regards
The deep-learning autoencoder is always unsupervised learning. The "supervised" part of the article you link to is to evaluate how well it did.
The following example (taken from ch.7 of my book, Practical Machine Learning with H2O, where I try all the H2O unsupervised algorithms on the same data set - please excuse the plug) takes 563 features, and tries to encode them into just two hidden nodes.
m <- h2o.deeplearning(
2:564, training_frame = tfidf,
hidden = c(2), auto-encoder = T, activation = "Tanh"
)
f <- h2o.deepfeatures(m, tfidf, layer = 1)
The second command there extracts the hidden node weights. f is a data frame, with two numeric columns, and one row for every row in the tfidf source data. I chose just two hidden nodes so that I could plot the clusters:
Results will change on each run. You can (maybe) get better results with stacked auto-encoders, or using more hidden nodes (but then you cannot plot them). Here I felt the results were limited by the data.
BTW, I made the above plot with this code:
d <- as.matrix(f[1:30,]) #Just first 30, to avoid over-cluttering
labels <- as.vector(tfidf[1:30, 1])
plot(d, pch = 17) #Triangle
text(d, labels, pos = 3) #pos=3 means above
(P.S. The original data came from Brandon Rose's excellent article on using NLTK. )
In some aspects encoding data and clustering data share some overlapping theory. As a result, you can use Autoencoders to cluster(encode) data.
A simple example to visualize is if you have a set of training data that you suspect has two primary classes. Such as voter history data for republicans and democrats. If you take an Autoencoder and encode it to two dimensions then plot it on a scatter plot, this clustering becomes more clear. Below is a sample result from one of my models. You can see a noticeable split between the two classes as well as a bit of expected overlap.
The code can be found here
This method does not require only two binary classes, you could also train on as many different classes as you wish. Two polarized classes is just easier to visualize.
This method is not limited to two output dimensions, that was just for plotting convenience. In fact, you may find it difficult to meaningfully map certain, large dimension spaces to such a small space.
In cases where the encoded (clustered) layer is larger in dimension it is not as clear to "visualize" feature clusters. This is where it gets a bit more difficult, as you'll have to use some form of supervised learning to map the encoded(clustered) features to your training labels.
A couple ways to determine what class features belong to is to pump the data into knn-clustering algorithm. Or, what I prefer to do is to take the encoded vectors and pass them to a standard back-error propagation neural network. Note that depending on your data you may find that just pumping the data straight into your back-propagation neural network is sufficient.