Deep Learning: Architectures & Methods an der TU Darmstadt | Karteikarten & Zusammenfassungen

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What is the goal of Deep Architectures?

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Deep learning methods aim at

  • learning feature hierarchies
  • where features from higher levels of the hierarchy are formed by lower level features
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Are multiple hidden layers better than one hidden layer?

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In practice, NN with multiple hidden layers work better than with a single hidden layer

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What is hype?

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Hype: "extravagant or intensive publicity or promotion"

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What is hope?

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Hope: "expectation of fulfillment or success"

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What are the milestones of DL?

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  • Digit Recognition
  • Image Classification
  • Speech Recognition
  • Language Translation
  • Deep Reinforcement Learning
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Why is the success of DNNs surprising?

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  • From both complexity and learning theory perspectives, simple networks are very limited
  • The most successful DNN training algorithm is a version of gradient descent which will only find local optima. In other words, it' a greedy algorithm.
  • Greedy algorithms are even more limited in what they can represent and how well they learn
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What is the task of Classification + Localization?

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  • Classification: C classes
    • Input: Image
    • Output: Class label
    • Evaluation metric: Accuracy
  • Localization: 
    • Input: Image
    • Output: Box in the image (x, y, w, h)
    • Evaluation metric: Intersection over Union
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Detection as Classification?

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Problem: Need to test many positions and scales, and use a computationally demanding classifier (CNN)

Solution: If your classifier is fast enough, just do it. Only look at a tiny subset of possible positions

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Object Detection: Evaluation

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  • We use a metric called "mean average precision" (mAP)
  • Compute average precision (AP) separately for each class, then average over classes
  • A detection is a true positive if it has IoU with a ground-truth box greater than some threshold (usually 0.5) (mAP@0.5)
  • Combine all detections from all test images to draw a precision/recall curve for each class; AP is area under the curve
  • TL;DR MAP is a number from 0 to 100; high is good
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What are problems with R-CNN?

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  1. Slow at test-time: need to run full forward pass of CNN for each region proposal
  2. SVMs and regressors are post-hoc: CNN features not updated in response to SVMs and regressors
  3. Complex multistage training pipeline
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What have we learnt about BN?

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  • BNs encode joint distributions 
  • They are DAGs (nodes=RVs, edges=dependencies)
  • Inference is NP-hard
  • Variable Elimination is one of the most basic inference approaches; there are many other inference approaches
  • We have skipped learning BNs
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What have we learnt about SPNs?

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Sum-product networks (SPNs)

  • DAG of sums and products
  • They are instances of Arithmetic Circuits (ACs)
  • Compactly represent partition function
  • Learn many layers of hidden variables

Efficient marginal inference

Easy learning

Can outperform well-known alternatives

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  • 117 Lernmaterialien

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Q:

What is the goal of Deep Architectures?

A:

Deep learning methods aim at

  • learning feature hierarchies
  • where features from higher levels of the hierarchy are formed by lower level features
Q:

Are multiple hidden layers better than one hidden layer?

A:

In practice, NN with multiple hidden layers work better than with a single hidden layer

Q:

What is hype?

A:

Hype: "extravagant or intensive publicity or promotion"

Q:

What is hope?

A:

Hope: "expectation of fulfillment or success"

Q:

What are the milestones of DL?

A:
  • Digit Recognition
  • Image Classification
  • Speech Recognition
  • Language Translation
  • Deep Reinforcement Learning
Mehr Karteikarten anzeigen
Q:

Why is the success of DNNs surprising?

A:
  • From both complexity and learning theory perspectives, simple networks are very limited
  • The most successful DNN training algorithm is a version of gradient descent which will only find local optima. In other words, it' a greedy algorithm.
  • Greedy algorithms are even more limited in what they can represent and how well they learn
Q:

What is the task of Classification + Localization?

A:
  • Classification: C classes
    • Input: Image
    • Output: Class label
    • Evaluation metric: Accuracy
  • Localization: 
    • Input: Image
    • Output: Box in the image (x, y, w, h)
    • Evaluation metric: Intersection over Union
Q:

Detection as Classification?

A:

Problem: Need to test many positions and scales, and use a computationally demanding classifier (CNN)

Solution: If your classifier is fast enough, just do it. Only look at a tiny subset of possible positions

Q:

Object Detection: Evaluation

A:
  • We use a metric called "mean average precision" (mAP)
  • Compute average precision (AP) separately for each class, then average over classes
  • A detection is a true positive if it has IoU with a ground-truth box greater than some threshold (usually 0.5) (mAP@0.5)
  • Combine all detections from all test images to draw a precision/recall curve for each class; AP is area under the curve
  • TL;DR MAP is a number from 0 to 100; high is good
Q:

What are problems with R-CNN?

A:
  1. Slow at test-time: need to run full forward pass of CNN for each region proposal
  2. SVMs and regressors are post-hoc: CNN features not updated in response to SVMs and regressors
  3. Complex multistage training pipeline
Q:

What have we learnt about BN?

A:
  • BNs encode joint distributions 
  • They are DAGs (nodes=RVs, edges=dependencies)
  • Inference is NP-hard
  • Variable Elimination is one of the most basic inference approaches; there are many other inference approaches
  • We have skipped learning BNs
Q:

What have we learnt about SPNs?

A:

Sum-product networks (SPNs)

  • DAG of sums and products
  • They are instances of Arithmetic Circuits (ACs)
  • Compactly represent partition function
  • Learn many layers of hidden variables

Efficient marginal inference

Easy learning

Can outperform well-known alternatives

Deep Learning: Architectures & Methods

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