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Henry AI Labs

Deep Learning Research Blog

Henry AI Labs

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Surveys in Deep Learning Topics

Generative Adversarial Networks

The Dueling Neural Network framework is one of the most interesting ideas in Deep Learning. This survey covers the basics of GANs and a discussion of the many papers released in this area of research

Data Augmentation

Data Augmentation refers to the practice of artificially inflating datasets thrgouh label-preserving operations. This is done to improve the generalization performance of Deep Neural Networks. This survey discusses many different ways of doing data augmentation, and research efforts building on these ideas.


Meta-Learning methods aim to learn the internal characteristics of Neural Networks. This can be done as easily as searching through hyperparameters, all the way up to learning the exact update step after each training iteration. This survey discusses papers such as Neural Architecture Search and AutoAugment.

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Super-Resolution networks learn to map a low-resolution image to it's high resolution counterpart. This survey discusses advances in the field and the use of Deep Learning. This includes techniques sucha s feature and adversarial losses.

Computer Vision

Computer Vision generally refers to the idea of teaching Computers and Algorithms to interpret the visual world. Deep Learning has had a massive role in the advancement of Computer Vision. This survey discusses many ways in which Deep Learning is applied to Computer Vision tasks.

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What is Henry AI Labs?

Henry AI Labs is a Deep Learning research group with remote researchers and writers. We write articles explaining Deep Learning research papers, surveys on general topics such as Generative Adversarial Networks or Unsupervised Language models, and analysis of popular news in Deep Learning such as the release of Open AI's gpt-2 model or Tensorflow 2.0. Thanks for visiting Henry AI Labs!

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