H13-311_V3.5 · Question #68
Convolutional neural networks are more suitable for image recognition problems than cyclic neural networks.
The correct answer is A. True. Convolutional neural networks (CNNs) are architecturally designed to exploit the spatial structure of images: their convolutional filters detect local features such as edges, textures, and shapes, and pooling layers provide translation invariance, meaning a feature is…
Question
Convolutional neural networks are more suitable for image recognition problems than cyclic neural networks.
Options
- ATrue
- BFalse
How the community answered
(15 responses)- A73% (11)
- B27% (4)
Explanation
Convolutional neural networks (CNNs) are architecturally designed to exploit the spatial structure of images: their convolutional filters detect local features such as edges, textures, and shapes, and pooling layers provide translation invariance, meaning a feature is recognized regardless of where it appears in the image. These properties make CNNs highly efficient and accurate for image recognition tasks. Option B is wrong because recurrent (cyclic) neural networks, while powerful for sequential data like text or time series, lack inherent mechanisms for capturing 2D spatial relationships and would require far more parameters to achieve comparable image recognition performance. The structural mismatch between RNNs and image data makes them a poor default choice for vision tasks.
Memory tip: Match the architecture to the data shape. Convolutional = spatial grids (images). Recurrent = sequences over time (text, audio).
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