Understanding the Effective Receptive Field in Deep Convolutional Neural Networks¶
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2016). Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
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Primes¶
- Receptive Field
- Convolutional neural networks: each filter responds to a local input patch, deeper layers build larger effective receptive fields by composition, and field size is a primary design parameter computable in advance from kernel, stride, dilation, and depth.
This sourceShows the effective receptive field is Gaussian and a fraction of the theoretical field, growing with depth and computable from kernel, stride, dilation, and depth.
- Convolutional neural networks: each filter responds to a local input patch, deeper layers build larger effective receptive fields by composition, and field size is a primary design parameter computable in advance from kernel, stride, dilation, and depth.
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