DL_Riddle_Quiz 1

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DL_RIDDLE_QUIZ_1

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Sivaranjini S

India

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DL_Riddle_Quiz 1Version en ligne DL_RIDDLE_QUIZ_1 par Sivaranjini S 1 I am the family of neural networks that operates over hierarchical input defined by a parse tree. Hints 1 At every parent node I combine my children using a shared weight matrix. 2 I am the alternative name for a transposed convolution emphasising its mathematical role as the gradient-flow operation of a forward strided convolution. Hints 1 I am a misleading term — I am NOT the inverse of convolution and I do NOT recover the original input. 2 People often use my name even though it is technically incorrect. 3 I am the segmentation loss most directly derived from the F1 score. Hints 1 I treat predicted and ground-truth foreground masks symmetrically as sets and penalise their lack of overlap. 2 I struggle when both masks are empty; people add a small ε to my denominator to avoid division by zero. 4 I am U-Net's mechanism for fusing high-resolution encoder features with upsampled decoder features. Hints 1 I do NOT use addition; I append along the channel dimension. 2 DenseNet is the architecture most associated with applying me densely between every pair of layers in a block. 5 I am a small fully-convolutional network that slides a 3×3 window over a shared backbone feature map. Hints 1 I classify each anchor as object/background and regress its bounding-box offset. 2 I replaced selective search and helped make object detection nearly real-time.

DL_Riddle_Quiz 1Version en ligne

DL_RIDDLE_QUIZ_1

par Sivaranjini S

I am the family of neural networks that operates over hierarchical input defined by a parse tree.

Hints

At every parent node I combine my children using a shared weight matrix.

I am the alternative name for a transposed convolution emphasising its mathematical role as the gradient-flow operation of a forward strided convolution.

Hints

I am a misleading term — I am NOT the inverse of convolution and I do NOT recover the original input.

People often use my name even though it is technically incorrect.

I am the segmentation loss most directly derived from the F1 score.

Hints

I treat predicted and ground-truth foreground masks symmetrically as sets and penalise their lack of overlap.

I struggle when both masks are empty; people add a small ε to my denominator to avoid division by zero.

I am U-Net's mechanism for fusing high-resolution encoder features with upsampled decoder features.

Hints

I do NOT use addition; I append along the channel dimension.

DenseNet is the architecture most associated with applying me densely between every pair of layers in a block.

I am a small fully-convolutional network that slides a 3×3 window over a shared backbone feature map.

Hints

I classify each anchor as object/background and regress its bounding-box offset.

I replaced selective search and helped make object detection nearly real-time.

Vous pouvez intégrer le jeu dans un LMS compatible avec LTI 1.1 ou LTI 1.3 comme Canvas, Moodle ou Blackboard. Les scores seront ainsi automatiquement enregistrés dans le carnet de notes de la plateforme.

DL_Riddle_Quiz 1

Devinette

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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DL_Riddle_Quiz 1Version en ligne

par Sivaranjini S

I am the family of neural networks that operates over hierarchical input defined by a parse tree.

Hints

I am the alternative name for a transposed convolution emphasising its mathematical role as the gradient-flow operation of a forward strided convolution.

Hints

I am the segmentation loss most directly derived from the F1 score.

Hints

I am U-Net's mechanism for fusing high-resolution encoder features with upsampled decoder features.

Hints

I am a small fully-convolutional network that slides a 3×3 window over a shared backbone feature map.

Hints

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