DL_Yes/NO_Quiz7

Yes or No

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About this activity

True or false

Created by

Sivaranjini S

India

Download the paper version to play

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DL_Yes/NO_Quiz7Online version True or false by Sivaranjini S 1 Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals. Yes No 2 DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output. Yes No 3 In BPTT, gradients are computed only for the final time step. Yes No 4 Transposed convolution is used to reduce spatial dimensions in segmentation networks Yes No 5 In GANs, the generator and discriminator are trained simultaneously in a competitive manner. Yes No 6 FPN relies on multiple resized input images like traditional image pyramids. Yes No 7 Skip connections increase the number of learnable parameters significantly. Yes No 8 Two-stage detectors first classify objects and then generate region proposals. Yes No 9 A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set. Yes No 10 The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator. Yes No

DL_Yes/NO_Quiz7Online version

True or false

by Sivaranjini S

Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals.

Yes

DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output.

Yes

In BPTT, gradients are computed only for the final time step.

Yes

Transposed convolution is used to reduce spatial dimensions in segmentation networks

Yes

In GANs, the generator and discriminator are trained simultaneously in a competitive manner.

Yes

FPN relies on multiple resized input images like traditional image pyramids.

Yes

Skip connections increase the number of learnable parameters significantly.

Yes

Two-stage detectors first classify objects and then generate region proposals.

Yes

A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set.

Yes

The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator.

Yes

You can integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

DL_Yes/NO_Quiz7

Yes or No

About this activity

Created by

Download the paper version to play

Top Games

Yes or No

Foot and Ankle Anatomy Quiz

Yes or No

Is it created with Artificial Intelligence? - Complete game

Yes or No

Adjective Checker

Yes or No

Is it true?

Yes or No

Grammys for best song/record

DL_Yes/NO_Quiz7Online version

by Sivaranjini S

Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals.

DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output.

In BPTT, gradients are computed only for the final time step.

Transposed convolution is used to reduce spatial dimensions in segmentation networks

In GANs, the generator and discriminator are trained simultaneously in a competitive manner.

FPN relies on multiple resized input images like traditional image pyramids.

Skip connections increase the number of learnable parameters significantly.

Two-stage detectors first classify objects and then generate region proposals.

A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set.

The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator.

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