6
votes
Accepted
Is it true that untrained CNNs can be used as feature extractors?
Yes, it has been demonstrated that the main factor for CNNs to work is its architecture, which exploits locality during the feature extraction. A CNN with random weights will do a random partition of ...
- 521
5
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 ...
- 41.4k
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
5
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 ...
- 41.4k
5
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 ...
- 1,987
4
votes
How to convert color information to 1D feature vector?
My 2 suggestions would be to:
Sum the hex coding of the color multiplied by the prevalence. For example [80, 80, 80] (grey) is used 7% of the time so ...
- 259
4
votes
How translation invariance is achieved in CNNs?
Take a vector: $V_1 = [v_1, v_2, ..., v_n]$
Calculate the max: $m_1 = \max V_1 = v_i$
Shuffle the vector: $V_2 = mix(V_1)$
Calculate the max: $m_2 = \max V_2 = v_j$
The only possible outcome is that $...
- 2,632
3
votes
How to convert color information to 1D feature vector?
I think the tool you found is useful for a human and to get nice visuals but I also think it's totally useless for feature extraction.
If you want to pass explicit information about colors simply ...
- 5,428
3
votes
Order of features learned by DNNs during training?
The order in which features are learned by DNNs during training is typically random & depends on a number of factors:
Including the specific architecture of the DNN
The type of data being used ...
- 1,134
3
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 ...
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 ...
- 2,389
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 ...
- 3,213
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 ...
- 854
2
votes
Accepted
How to calculate a meaningful distance between multidimensional tensors
You could try an earth mover distance in 2d or 3d over the image? For example you could follow this example, but call it sequentially. The idea would be something like the following (untested and ...
- 206
2
votes
Accepted
How does a Machine Learning model predict this classification problem?
when I don't give new sentence features (F1, F2) to my model and don't specify any procedure to calculate the features for new inputs, how the model can predict the sentiment of my new sentences?
It ...
- 3,186
2
votes
Accepted
Can DeepSort be made to track objects beside people?
In my experience, if you have really discriminative objects with distinct features then yes! The original DeepSORT's reid model can be borrowed to track those things.
But for the best result, you ...
- 124
2
votes
How translation invariance is achieved in CNNs?
When you apply a convolutional layer to an image $x$, you obtain a certain list of values:
$$h_1(x), h_2(x), h_3(x), ..., h_n(x) \tag 1$$
where each $h_i$ is just the function that applies the ...
- 292
2
votes
Can a concept/feature be represented using more than one layer of a Neural Network?
You cannot reason in a mathematical way over features in my opinion, as they are not defined. However, you can think of deep neurons as a hierarchy of always more high level concepts, as observed in ...
- 2,632
2
votes
Accepted
Is geodesic distance between two similar photos less than the Euclidean distance between them? If so, why?
Imagine your images are embedded in a space that is a circle. Now, to simplify, we put an extra condition in which each embedding (i.e., an image represented somehow in 2 dimensions) must lie on the ...
- 3,186
2
votes
Accepted
What is $\mathbf{S}$ (sample covariance matrix) in image compression based on PCA?
Good question! There's actually some ambiguity here: it's possible to consider the lower-dimensional projection with respect to the pixels within a single image or across a dataset of images.
A ...
- 1,399
2
votes
the best choice to reduce a features vector
Feature selection -- the case in which the features are highly correlated is the prototypical case in which you want to select a subset of independent features that allows for an equal performance. ...
- 31
2
votes
how to determine the number of units for dense layer for transfer learning?
Not only the units but also the number of layers... you can reason over something like "how complex is your task", but usually we resort to grid search over some educated guesses (like 2/3 ...
- 2,632
1
vote
What's the purpose of "feature extraction using a pretrained model"?
A neural network typically processes an image by taking all of the pixel-level data as input, and passing that through hidden feature layers which extract useful but more complex aspects of the image. ...
- 368
1
vote
What's the purpose of "feature extraction using a pretrained model"?
A feature extractor is just the first part of a CNN (convolutional neural network), which is a neural-network model that can process images and so solve computer vision tasks.
As shown in the image ...
- 3,186
1
vote
Feature Extraction for timeseries temperature signal
Cross correlation
A high cross-correlation value indicates a strong correlation between the two signals, which can be useful for identifying the specific event that one of the signals is sensitive to.
...
- 111
1
vote
Accepted
How to extract the high-level features of YOLOv5?
Set export = True at the yolo head. If the net is the AutoShape instance, you can achieve it ...
1
vote
How to use image feature extraction as input to another model?
Most pre-trained ResNet are pretty capable of returning a rather informative feature representation, even without fine-tuning. This, surprisingly, seems to be the case also for image datasets that do ...
- 371
1
vote
Make an NN utilize other NNs as part of its decision process
In tensorflow models can be their own layers as part of a larger architecture.
(If you want to see an example you can check out a notebook I have here and here)
Specifically what you are trying to do ...
- 111
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