Questions tagged [feature-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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1answer
40 views

Where do the feature extraction and representation learning differ?

Feature selection is a process of selecting a subset of features that contribute the most. Feature extraction allows to get new features that are not actually present in the given set of features. ...
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0answers
15 views

Controlling mutual information in latent variables

Recently, I read some interesting papers on mutual information (MI) estimation in high dimensional variables using neural networks [Belghazi et al., 2018][Poole et al., 2019]. These methods besides ...
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0answers
12 views

Why does self-supervised representation learning (such as SimpleSiam) use a ResNet encoder that is trained in a supervised fashion?

Can anybody explain to me why does self-supervised representation learning on images using Siamese neural networks (such as SimpleSiam (https://arxiv.org/abs/2011.10566), SimCLR, Boyl) use a ResNet ...
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1answer
61 views

Why different images of the same person, under some restrictions, are in a 50 dimension manifold?

In this lecture (starting from 1:31:00) the professor says that the set of all images of a person lives in a low dimensional surface (compared the the set of all possible images). And he says that the ...
3
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2answers
78 views

How do we know that the neurons of an artificial neural network start by learning small features?

I'd like to ask you how do we know that neural networks start by learning small, basic features or "parts" of the data and then use them to build up more complex features as we go through ...
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1answer
67 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 ...
2
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0answers
30 views

Graph Neural Networks: Quesitons about different GCN Architectures

This might be moreof 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 Kipf/ ...
2
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1answer
592 views

How to generate labels for self-supervised training?

I've been reading a lot lately about self-supervised learning and I didn't understand very well how to generate the desired label for a given image. Let's say that I have an image classification task, ...
1
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0answers
48 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 ...
2
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1answer
154 views

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-...
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0answers
16 views

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 ...
2
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1answer
54 views

Can a neural network learn to predict a number given a binarized image of a rectangle?

Let's assume that we have a regression problem. Our input is just binarized image that contains a single rectangle and we want to predict just a float number. Actually, this floating-point number ...
5
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2answers
79 views

What are examples of approaches to dimensionality reduction of feature vectors?

Given a pre-trained CNN model, I extract feature vector of images in reference and query dataset with several thousands of elements. I would like to apply some augmentation techniques to reduce the ...
4
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2answers
553 views

How to understand the concept of self-supervised learning in AI?

I am new to self-supervised learning and it all seems a little magical at the moment. The only way I can get an intuitive understanding is to assume that, for real-world problems, features are still ...
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0answers
56 views

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 ...
2
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1answer
38 views

What does “class-level discriminative feature representation” mean in the paper “Semi-Supervised Deep Learning with Memory”?

I am reading the paper Semi-Supervised Deep Learning with Memory (2018) by Yanbei Chen et al. The topic is the classification of images using semi-supervised learning. The authors use a term on page ...
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0answers
36 views

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 ...
72
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3answers
64k views

What is self-supervised learning in machine learning?

What is self-supervised learning in machine learning? How is it different from supervised learning?
1
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1answer
86 views

When should I use feature learning as opposed to feature engineering?

With the advancement of deep learning and a few others automated features learning techniques, manual feature engineering started becoming obsolete. Any suggestion on when to use manual feature ...
4
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2answers
4k views

What is feature embedding in the context of convolutional neural networks?

What are feature embeddings in the context of convolutional neural networks? Is it related to bottleneck features or feature vectors?