5
votes
Accepted
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 ...
- 339
4
votes
Accepted
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 ...
- 33k
3
votes
Accepted
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 ...
- 33k
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 ...
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 ...
- 2,229
2
votes
Accepted
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 ...
- 3,093
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 ...
- 1,373
2
votes
Accepted
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 ...
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 ...
- 121
2
votes
Accepted
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 ...
- 794
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 ...
- 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 ...
- 11
1
vote
Accepted
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 ...
- 1,773
1
vote
Accepted
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 ...
- 549
1
vote
Accepted
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 ...
- 706
1
vote
Accepted
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 ...
- 23.2k
1
vote
Accepted
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.
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 ...
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 ...
- 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 ...
- 5,047
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