Which of the following statements are true?
Tanh activation function can be represented as below in terms of sigmoid activation function:
tanh(x) = 2σ(2x) − 1, where σ = sigmoid activation function
The derivative of the Sigmoid activation function is symmetric around the origin.
Gradient of sigmoid neuron at saturation explodes
sigmoid activation is centered around 0.5 whereas tanh activation is centered around 0.
Which of the below properties are not exhibited by the gradient-based explanation generation method, Grad-CAM? (Select all that apply)
Grad-CAM saliency map only computes gradients for important pixels while calculating weights, so pixels that contribute more to a specific class are considered
Grad-CAM can’t generate explanation directly from the given CNN and needs additional retraining
Grad-CAM is very reliable at identifying multiple instances of objects with occlusions
Grad-CAM can help localize objects without positional supervision
Which of the following statements accurately describes the visualization techniques used in Convolutional Neural Networks (CNNs)? (Select all that apply)
Activation maximization methods identify input image region that maximally activates specific neurons in the network.
Filter visualization techniques display the learned weights of convolutional filters in the network.
Kernel visualization involves directly observing the values of convolutional filters during network training.
Principal Component Analysis (PCA) is commonly employed to reduce the dimensionality of feature maps for visualization purposes.
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The IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) Quiz 2 paper sat on 24 Mar 2024, in the January 2024 term: 28 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Deep Learning for Computer Vision Quiz 2 24 Mar 2024 at a glance |
|---|---|
| Term | January 2024 term |
| Subject | Deep Learning for Computer Vision |
| Course code | BSDA5006 |
| Questions | 28 |
| Marks | 50 |
| Duration | 120 min |
| MSQ | 6 |
| Numerical | 10 |
| MCQ | 11 |
| Written | 1 |
| Official paper | IIT M DEGREE AN EXAM QDB2 24 Mar 2024 |
| Negative marking | No negative marking. |
| Updated |
