Deep Learning References and Resources

You can find a selection of articles, blog posts, models, and other resources about deep learning on this page.

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Deep Architectures

A comprehensive overview of deep learning architectures can be found at: Towards Data Science. It covers all the important architectures, such as:

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DeepLabv3+

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Batch Size

Batch sizes over 32 are not useful to increase performance, "…the best performance has been consistently obtained for mini-batch sizes between m=2 and m=32 , which contrasts with recent work advocating the use of mini-batch sizes in the thousands."

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Res-Net

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Fully Convolutional Networks (FCN)

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U-Net

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Segmentation Networks

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Optimization Methods

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Batch Normalization

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Nearest Neighbor Upsampling

Deconvolution/Transposed convolution can lead to checkerboard patterns especially in strong colors because of kernels overlapping and maybe because of gradient artifacts.

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Domain Adaptation / Transfer Learning