Questions tagged [representation-learning]

For questions related to feature learning (also known as representation learning), which is a set of techniques that can learn the features associated with the raw data. It is similar to feature engineering, but, in the case of feature learning, the features are learned and not handcrafted.

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Does self-supervised learning require auxiliary tasks?

Self-supervised learning algorithms provide labels automatically. But, it is not clear what else is required for an algorithm to fall under the category "self-supervised": Some say, self-...
Make42's user avatar
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2 votes
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Does this learning scenario have a name? If so, can someone point me to relevant literature?

I am faced with a problem which I bet was already solved before, but that I had never seen. Perhaps by discussing it abstractly, someone can point me to relevant literature. It goes like this: I have ...
Alek Fröhlich's user avatar
2 votes
1 answer
124 views

Multi-objective training involving maximization of one loss function and minimization of another

I need my model to predict $s$ from my data $x$. Additionally, I need the model to not use signals in $x$ that are predictive of a separate target $a$. My approach is to transform $x$ into a ...
ChargeShivers's user avatar
2 votes
0 answers
63 views

Given a 2-layer GCN, can we choose the dimensions of the 2nd weight matrix, such that this architecture has the same capacity as a 1-layer GCN?

This might be more of a question about nested function classes: For $k$ class node classification in a graph with $n$ nodes, and $d$ feature vector. I want to compare Architecture I: the GCN model of ...
Tinatim's user avatar
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1 vote
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Toy dataset: Radial VAE

I'm evaluating disentanglement in toy datasets seeing as we have such little understanding of the phenomena. I'm using various tools from differential geometry. Now I want to train a VAE on the ...
John Miller's user avatar
1 vote
0 answers
43 views

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
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In Graph Neural Network is Message Passing Step Agnostic of Output Values during Training?

So Graph Neural Networks is about representation learning where initially representation of graph is learned in the form of node embeddings. My question is: Are the output values back propagated and ...
user0193's user avatar
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Is there some way for us to know if the neural network internally finds an association between labels?

I have a question about the association between labels. Say my neural network performs multi-labeling in its output layer. Now, if one of the labels is for whether a person lives in city $X$, another ...
Abhiram Natarajan's user avatar
1 vote
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How to use K-means clustering to visualise learnt features of a CNN model?

Recently, I was going through the paper Intriguing Properties of Contrastive Losses. In the paper (section 3.2), the authors try to determine how well the SimCLR framework has allowed the ResNet50 ...
VEDANT JOSHI's user avatar
1 vote
0 answers
86 views

Extending patch based image classification into image classification

I am trying to classify tampered, pristine images from set of images, in that I have built a network in which I would divide the image into multiple overlapping patches and then classify them into ...
kiran's user avatar
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Convolutional Feature Encoding Methods in DCNN

In Computer Vision, feature encoding methods are used on pre-trained DCNN to increase the feature robustness to certain conditions such as viewpoint/appearance variations ref. I was just wondering if ...
doplano's user avatar
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Plot class activation heatmap of Caffe Model in Python

Given the following 3 research papers, the authors have shown different heatmap graphical representations for features of the trained CNN models: On the performance of Convnet feature for place ...
doplano's user avatar
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Is it possible to use adversarial training to learn invariant features?

Given a set of time series data that are generated from different sites where all sites are investigating the same objective but with slightly different protocols. Is it possible to use adversarial ...
Mwiza Kunda's user avatar
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35 views

How are the softmax normalized weights in ELMo actually learned and computed?

I was reading the ELMo paper, and they speak of task-specific representations of words (or tokens generally speaking) by using the following equation: $ELMo_{k}^{task} = \gamma^{task}\sum_{0}^{L}{s_{j}...
Propr's user avatar
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1 answer
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A technique of aggregating many input images to a single representation of the relevant features within

I have a few thousand images and I would like to generate a representation of the foreground patterns within - a composition of all of its features, so to speak. In simple terms: take 10000 images of ...
mluerig's user avatar
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1 answer
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How can I adapt a trained neural network model to learn from newer data containing additional features?

We shall assume that we have a trained neural network model for some task $A$. The dataset used to train the model contained some $n$ features per sample. Using this dataset, we were able to train a ...
user52084's user avatar
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0 answers
127 views

Train separate AutoEncoder's on each class or one AE for all classes to learn features?

I'm working on a project where the dataset contains time series of three classes, depending on the shape of the series. I want to learn the representations of these series as vectors, so naturally I ...
Elise Le's user avatar
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2 answers
501 views

classification of unseen classes of image in open set classification

I have a scanned image, and they need to be classified in one of the pre-defined image classes, so that it can be sorted. However, the problem is the open nature of the classes. At testing time, new ...
Rambo_john's user avatar