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Deep learning has become the de facto standard method in most image processing problems. In the past few years, deep learning algorithms have met and exceeded human-level performance in image recognition. Nevertheless, training deep learning networks on a large data set remains very challenging. The sheer amount of computation needed to train a convolutional neural network can take months on large data sets. Combining that with the black art of hyper-parameter tuning, the community desperately needs tools to help train deep learning networks on multiple servers with multiple GPUs.

Recognition of visual content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on understanding visual content using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now striving to bridge vision with natural language, which can be regarded as the ultimate goal of visual understanding.

Hyperspectral imagery, also called imaging spectroscopy, refers to images with a large number (typically a few hundreds) of narrow and contiguous spectral bands, covering a wide range of the electromagnetic spectrum from the visible to the infrared domain. Hyperspectral data are able to provide a very fine description of the chemical components in the sensed materials and ensure their detection, discrimination and characterization.

Hyperspectral imagery, also called imaging spectroscopy, refers to images with a large number (typically a few hundreds) of narrow and contiguous spectral bands, covering a wide range of the electromagnetic spectrum from the visible to the infrared domain. Hyperspectral data are able to provide a very fine description of the chemical components in the sensed materials and ensure their detection, discrimination and characterization.