The Two-Headed Neural Network Shaking Up Image Recognition

The Two-Headed Neural Network Shaking Up Image Recognition

Deep neural networks have a big problem — they're constantly hungry for. The Two-Headed Neural Network Shaking Up Image Recognition.

Deep neural networks have a big problem — they’re constantly hungry for data. When there is too little data — an amount that would be acceptable for other algorithms — deep neural networks have tremendous difficulty generalizing. This phenomenon highlights a gap between human and machine cognition; humans can learn complex patterns with few training examples (albeit at a slower rate).

The Need for Machines That Think More Like Us

While research in self-supervised learning is growing to develop structures in which labels are completely unnecessary (labels are cleverly found in the training data itself), its use cases are restricted.

Semi-supervised learning, another quickly growing field, utilizes latent variables learned through unsupervised training to boost the performance of supervised learning. This is an important concept, but its scope is limited to use cases where there is a relatively large unsupervised-to-supervised data ratio, and where the unlabeled data is compatible with labeled data.

Perhaps one idea encapsulates all ideas — developing methods and architectures that make the most of limited labeled data; to make machines think a little more like humans do. A formal name is meta-learning, often referred to as ‘learning to learn’.

A common term used in meta-learning and natural language processing is ‘few-shot learning’ or ‘zero-shot learning’. These refer to being able to recognize new concepts with few or no (respectively) data teaching the model the concept beforehand. One example of zero-shot learning would be translating from English to German after being trained on English-to-French and French-to-German translation tasks.

data-science artificial-intelligence ai machine-learning deep-learning

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