Steel Bar Fault Detection Training Techniques Quiz
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Questions and Answers

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

  • Text summarization, machine translation, and sentiment analysis
  • Image recognition, audio synthesis, and video classification
  • Node classification, node similarity, and link prediction (correct)
  • Speech recognition, natural language processing, and anomaly detection

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

  • Training using labeled data (correct)
  • Training without using labeled data
  • Training without any predefined objectives
  • Training using reinforcement learning only

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

  • Vanishing gradients (correct)
  • Exploding gradients
  • Overfitting
  • Underfitting

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

    <p>Apply Layer Normalization to the batch of hidden state vectors</p> Signup and view all the answers

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

    <p>BiLSTMs would 'see the future' in a prediction task</p> Signup and view all the answers

    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?

    <p>The backbone network pre-trained on ImageNet does not help in detecting grayscale images with different aspect ratios.</p> Signup and view all the answers

    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?

    <p>It allows for better adjustment to objects with different aspect ratios compared to pre-trained models.</p> Signup and view all the answers

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

    <p>It prevents overfitting to specific image resolutions by using a variety of input scales.</p> Signup and view all the answers

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

    <p>It enhances shift invariance which is beneficial for detecting wear faults.</p> Signup and view all the answers

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