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

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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2 votes
Accepted

Best practice for handling letterboxed images for non fully-convolutional deep learning networks?

Padding is indeed the easiest solution. And if no bias is used then masking the extra values during the loss computation is also not necessary, since it's enough to use zero as padding value. You ...
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1 vote

How to label unsupervised data for deep learning multi-classification

"is it okay to use another machine learning technology such as K-Means clustering to label the data?" In computer vision there's an entire branch called automatic image annotation dedicated ...
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1 vote
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How to handle an unbalanced dataset when training object detection algorithms?

One thing to try first is Focal Loss. This particular loss works well for classification or object detection where your dataset is unbalanced and contains many classes. In short, the loss suppresses ...
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  • 41

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