Steel Bar Fault Detection Training Techniques Quiz

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9 Questions

What are some possible learning objectives that can be achieved with Graph Neural Networks?

Node classification, node similarity, and link prediction

In the context of training techniques for Graph Neural Networks, what does 'supervised' training refer to?

Training using labeled data

What issue might be indicated if the training loss for an LSTM quickly converges to a non-zero value?

Vanishing gradients

Which action could be used to improve model performance when observing quickly converging activation values close to 0 or 1 in LSTM gates?

Apply Layer Normalization to the batch of hidden state vectors

Why is using a BiLSTM instead of an LSTM not applicable when dealing with quickly converging activation values close to 0 or 1?

BiLSTMs would 'see the future' in a prediction task

What is the main reason provided in the text for not fine-tuning the detector module of a YOLO-v4 model pre-trained on pedestrian detection?

The backbone network pre-trained on ImageNet does not help in detecting grayscale images with different aspect ratios.

Why is designing a custom Feature Pyramid Network style architecture with a custom Region Proposal Network considered a valid choice for training in this context?

It allows for better adjustment to objects with different aspect ratios compared to pre-trained models.

How does training with multi-scale inputs contribute to a higher detection rate in this scenario?

It prevents overfitting to specific image resolutions by using a variety of input scales.

Why is applying blur filters before performing image downsampling considered a valid choice in this context?

It enhances shift invariance which is beneficial for detecting wear faults.

Test your knowledge on training techniques for detecting production and wear faults on steel bars using machine learning models. Determine which training techniques are most likely to lead to a higher detection rate for fault detection.

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