DL4CV at TU München

Flashcards and summaries for DL4CV at the TU München

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What is the Depth of Neural Network?

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Is it ok to treat dimensions separately in Batch Normalilization?

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Regularization Techniques?
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Exemplary flashcards for DL4CV at the TU München on StudySmarter:

Why Transfer Learning is good?

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How do we get from filters to a ConvNet?

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How should be the filter depth dimension?

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How can we restrict degree of freedom/reduced learnable parameters?
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Exemplary flashcards for DL4CV at the TU München on StudySmarter:

What was the first use of ReLU?

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What are Unpooling ways?

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What are the usage of 1*1 convolution?

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What is the connection between Autoencoder and PCA?
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Exemplary flashcards for DL4CV at the TU München on StudySmarter:

What does KL-Div Loss do in latent space?

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Exemplary flashcards for DL4CV at the TU München on StudySmarter:

DL4CV

What is the Depth of Neural Network?
Number of Layers

DL4CV

Is it ok to treat dimensions separately in Batch Normalilization?
Shown empirically that even if features are not decorrelated, convergence is still faster with this method

DL4CV

Regularization Techniques?
Weight Decay Data Augmentation Early Stopping Ensemble Bagging Dropout

DL4CV

Why Transfer Learning is good?
we learning different level of features which we can use for other settings

DL4CV

How do we get from filters to a ConvNet?
The idea is optimize for filter banks Filters are spatially-invariant Extract features at locations Multiple feature banks per location

DL4CV

How should be the filter depth dimension?
Depth dimension *must* match input depth dimension;i.e., filter extends the full depth of the

DL4CV

How can we restrict degree of freedom/reduced learnable parameters?
1) In conVnets connections are structured (everything is not connected with everything) 2) in convolution wen we apply a filters to different spatial location the weights are shared

DL4CV

What was the first use of ReLU?
AlexNet

DL4CV

What are Unpooling ways?
1) Nearest Neighbor 2) Bed of Nails

DL4CV

What are the usage of 1*1 convolution?
1) we can use it to have fully convolutional networks (remove FC layer) 2) reduced dimention

DL4CV

What is the connection between Autoencoder and PCA?
PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders. A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means that the W W found by AE and PCA won't be the same--but the subspace spanned by the respective W W's will.

DL4CV

What does KL-Div Loss do in latent space?
forcing a unit Gaussian distribution so the latent vector becomes a distribution

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DBS at

TU Ilmenau

DS at

Pädagogische Hochschule Karlsruhe

DaZ at

Freie Universität Berlin

DSB at

SRH Fernhochschule - The Mobile University

DB at

Fachhochschule Vorarlberg

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