Addressing Catastrophic Forgetting In ML With ANML & Meta-Learning

What is the one thing neural networks have been battling with over the years? Forgetting. Unlike human beings, where we don’t forget things like riding a bike or the rules of a sport even after getting back to them after…

Exciting AI Researches To Look Up To In 2020

The last decade has been exciting for artificial intelligence. It has gone from esoteric to mainstream very quickly, thanks to ingenious work by researchers, the democratisation of technologies by top companies and, of course, enhanced hardware. In this article, we…

How To Make Meta-learning More Effective

Meta-learning was introduced to make machine learning models to learn new skills and adapt to the ever changing environments in the presence of finite training precedents. The main objective of this approach is to find model agnostic solutions. One highly…

Artificial  Intelligence Brings Mona Lisa To Life Using GANs  

It is believed that Leonardo Da Vinci took more than a decade to paint a realistic version of Lisa del Giocondo which also happens to be the world’s most famous portrait ‘Mona Lisa’. Da Vinci worked through his adult life…

Why You Should Try Model Agnostic Solutions When The Data Is Vague

When it comes to performing few-shot learning tasks, deep neural nets fail to live up to their expectation. Because in a few-shot learning task, the classifier has to make generalisation after every few examples from each class. Meta-learning came into…

Understanding Reptile: A Scalable Meta-learning Algorithm By OpenAI

With Machine Learning (ML) advancing its frontiers day by day, developing base algorithms to fulfill the needs of self-learning in machines is a challenging task. In meta-learning, as the algorithms encounter a horde of data, they become more prone to…

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