13 votes
Accepted

How can I deal with images of variable dimensions when doing image segmentation?

There are 2 problems you might face. Your neural net (in this case convolutional neural net) cannot physically accept images of different resolutions. This is usually the case if one has fully-...
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  • 384
8 votes
Accepted

Is it okay to use publicly available Instagram videos to train an AI?

Under US copyright law, this is probably fair use ...but beware of memorization. You may run into more trouble if the AI outputs things very similar to the original work. Also, consult a lawyer to ...
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  • 196
7 votes
Accepted

How many neurons would a network have after a training of 100k small images?

The neural network is typically a set size once it's created. You'd have to create a network big enough for your data-set.
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  • 358
5 votes

How to detect LEGO bricks by using a deep learning approach?

So I am assuming that you are trying to detect a lego brick from the image. One idea is that you can use transfer learning. Leveraging a pre-trained machine learning model is called transfer learning. ...
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5 votes
Accepted

Small size datasets for object detection, segmentation and localization

There are various dataset available such as Pascal VOC dataset: You can perform all your task with these. size of the dataset is as follows ADE20K Semantic Segmentation Dataset: you can perform ...
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  • 328
5 votes
Accepted

Why do we need both the validation set and test set?

The difference between the validation and test set in my opinion should be explained in this way: the validation set is meant to be used multiple times. the test set is meant to be used only once. ...
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5 votes
Accepted

What are "proxy data sets" in machine learning?

In computer science, if you say "A is a proxy for B", then it means that "A replaces B" (temporarily or not), or that "A is used as an intermediary for B". The term "...
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  • 33.8k
5 votes

How to generate labels for self-supervised training?

How can I generate the target label from the other data in the dataset? If you are asking how you can create the learning signal in SSL, when given an unlabelled dataset, for learning representations ...
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  • 33.8k
5 votes
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Should we also shuffle the test dataset when training with SGD?

Short answer Shuffling affects learning (i.e. the updates of the parameters of the model), but, during testing or validation, you are not learning. So, it should not make any difference whether you ...
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  • 33.8k
5 votes
Accepted

Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

Using the (unchecked) predictions of the model as training data is an approach known as "pseudo-labeling". It can help in certain situations, depending on the underlying structure of your ...
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  • 228
4 votes

What are some datasets to train an MLP on simple tasks?

There are a ton of sample datasets our there you can play with. A bunch of good ones install with R in the datasets package. Luckily you can download them independently if you're not an R user. Try ...
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  • 3,677
4 votes

Can recommendation systems be created for other data other than images?

Recommendation systems can be applied for anything, as long as you have sufficient training data. The most important inputs to the recommendation system are not "audio files or video files". ...
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  • 1,390
4 votes

How can I train a neural network for image classification when the dataset is small?

Use Fine Tuning You can simply use a pre-trained model on ImageNet, as this data set has multiple snakes classes. Then you can fine tune the model with your own small data set and outputs. See this ...
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4 votes
Accepted

What happens to the training data after your machine learning model has been trained?

In many cases, a production-ready model has everything it needs to make predictions without retaining training data. For example: a linear model might only need the coefficients, a decision tree just ...
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  • 291
4 votes

How could decision tree learning algorithms cope with imbalanced classes?

Decision Tree learners, on their own, are not a good way to deal with imbalanced data. The most commonly used algorithms, by default, make no attempt to address this problem. If you look carefully at ...
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4 votes

What is the reason for taking tuples as vectors rather than points?

They are equivalent. When we consider a particular instance as a vector, we are not literally imagining it as an arrow with it's head at the point coordinates and tail at the origin. It's just when ...
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4 votes
Accepted

How should I generate datasets for a SARSA agent when the environment is not simple?

I am wondering how to generate datasets when the environment is not as simple as a tic-tac-toe or a maze problem There is no difference in concept, which is why tic-tac-toe and maze problems are used ...
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  • 23.9k
4 votes
Accepted

Would it be possible to determine the dataset a neural network was trained on?

You can already do this with some neural networks, such as GANs and VAEs, which are generative models that learn a probability distribution over the inputs, so they learn how to produce e.g. images ...
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  • 33.8k
4 votes

Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

The answer is: It depends. What you describe is a strategy often used to save time and costs for labelling data. It is important that the data you have already labelled (the 20%) is representative of ...
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3 votes
Accepted

What is the effect of mislabeled training data?

I think the crucial point here is what you precisely mean by mislabelled. Google's image classifier will likely do a 'pretty good' job of retrieving images with the given subject included, but how ...
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3 votes

What are some concrete steps to deal with the vanishing gradient problem?

There is not single answer to the vanishing gradient problem. However, there a few things that can help. As mentioned in the comments, use of Rectified Linear Units (ReLU) as your activation function ...
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3 votes

Would this relatively small dataset be enough to train a CNN?

Your data set would be what is called "unbalanced' and this can lead to problems in developing an accurate classifier. The best thing to do (which you might not be able to do) is to find more ...
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  • 644
3 votes

How to classify language as friendly or aggressive with AI?

I did a little search and couldn't find any database that has ground truth for aggressiveness. This means that you need to build yourself a database. This might be huge undertaking. Take thousands of ...
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3 votes
Accepted

How can one find / collect data for, and come up with ideas for, using Deep Learning / AI to improve one's everyday life?

Speaking to the "collecting data" part of the question, I'll say this: Keep in mind that not everything requires massive amounts of data. Consider also that large amounts of data about all sorts of ...
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  • 3,677
3 votes

What are examples of techniques to prevent bias in artificial intelligence systems?

It's important to note that, ultimately, the statistical methods we currently use in ML research are just that: statistical methods. So, when they show some "bad behaviour", it's not because ...
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3 votes

How can I train a neural network for image classification when the dataset is small?

Besides using transfer learning described in other answer, you should consider using siamese network. This type of network is used in cases when one does not posess many examples of objects he wants ...
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3 votes

Traffic signs dataset

Look at Google's Open Image Dataset @ https://storage.googleapis.com/openimages/web/index.html They provide image-level labels, object bounding boxes, object segmentation masks, and visual ...
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3 votes
Accepted

Using GAN's to generate dataset for CNN training

I think you'll enjoy this work from Apple on improving the realism of synthetic images. Essentially what you need to do is generate a synthetic image then have your GAN modify the synthetic image so ...
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