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5 votes
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Is word embedding a form of feature extraction?

Though word-embedding is primarily a language modeling tool, it also acts as a feature extraction method because it helps transform raw data (characters in text documents) to a meaningful alignment of ...
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4 votes
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Where do the feature extraction and representation learning differ?

Feature extraction (FE) is not the same as representation learning (RL), but they are similar and related. You describe accurately what feature extraction typically refers to, i.e. the process of ...
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3 votes
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What are bag-of-features in computer vision?

Introduction Bag-of-features (BoF) (also known as bag-of-visual-words) is a method to represent the features of images (i.e. a feature extraction/generation/representation algorithm). BoF is inspired ...
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3 votes

How do I determine which relevant features have been learned during training in a CNN?

There are methods called "scoring systems" where you give a image scores such as "0.9 stripes, 0.0 red, 0.8 hair, ..." and use those scores to classify objects. It's an older idea, not used to ...
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2 votes

Is word embedding a form of feature extraction?

I think you guys are playing on semantics. If you consider feature extraction to be an unlearned preprocessing step to get inputs for your model, then no, word embeddings are not a feature extraction ...
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  • 2,229
2 votes
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What are the features get from a feature extraction using a CNN?

You get what we call high-level features, which are basically abstract representations of the parts that carry information in the image you want to classify. Imagine you want to classify a car. The ...
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2 votes

Are deep learning models suitable for training with sparse data?

The problem isn't the GAN but the implementation of its discriminator which is typically a convolutional neural network (CNN). CNNs have trouble with sparse data. They require dense data to learn ...
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2 votes
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When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Yes, neural networks learn features themselves freeing you from the need to manually engineer them. I will illustrate it here with a toy problem. Let's assume that we want to learn the areas of ...
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2 votes

When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Feature engineering may be necessary when one cannot achieve acceptable error rate — within a budget or in principle. NN may be stalling due to information bottleneck: too many pigeons, not enough ...
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2 votes
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Bag of Tricks: n-grams as additional features?

Yes, N-grams is about joining $n$ words as one single token. Keep in mind it will greatly increase your features size: If you originally have 1000 unique words, notice you could get up to 1000² 2-gram ...
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1 vote

Is the phrase "Feature Pyramid Network" refer to CNN only?

No, Feature Pyramid Networks does not only refer to CNNs. Take the recently trending "Swin Transformer" as an example. Swin Transformer is a variant of vision transformers, and they do not ...
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  • 271
1 vote

What is the difference between feature extraction and fine-tuning in transfer learning?

The difference between the two approaches (feature extraction vs fine-tuning) is well explained here: Fine Tuning vs Joint Training vs Feature Extraction Also, this paper evaluate the performance one ...
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1 vote
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Is it a good practice to pad signal before feature extraction?

Padding is a common practice both in image-processing (typically via CNNs) and in sequence-processing tasks (RNNs, Transformers). For CNNs all the standard convolutional layers - Conv1D, Conv2D and ...
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  • 1,773
1 vote
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How is a ResNet-50 used for deep feature extraction?

The authors use so-called embeddings, it's a form to represent the images in some meaningful vector form. The procedure to get embedding as follows. First, keep in mind most of the popular ...
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1 vote
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How can Image Caption work?

The Standard Image Captioning Pipeline is to train the model in a single batch(or mini-batch) i.e. get the features from the CNN Image encoder and then feed that into an RNN decoder (features + Real ...
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1 vote
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What is the difference between feature extraction with or without data augmentation?

There are two ways that you could perform data augmentation: Up front, by expanding the input dataset into a larger one, performing a range of changes to each input then storing the result. This ...
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1 vote
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Which type of feature extractor do you suggest to classify sensor data?

There could be multiple possible ways to extract the features. One would be to use RNNs for a temporal relationship as the input data is time-series.
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1 vote

Corner detection algorithm gives very high value for slanted edges?

I can't go into details of the algorithm but here's some intuition about what's apparently going wrong: The Sobel transformation identifies mostly-vertical and mostly-horizontal edges. For slanted ...
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1 vote

How to choose a suitable threshold value for the Shi-Tomasi corner detection algorithm?

The threshold is typically chosen empirically, so there is no exact answer. It's dependent on how many corners you wish to select, and how strict you want the detection, which could depend on the use ...
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  • 111
1 vote

Extract product information from email receipt HTML

It depends on the data. If it is structured like form data, then you might not need AI at all — simple regular expression patterns might be fine. This would apply for example to address data. If ...
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