Practical Few-Shot Learning
Deep learning now is achieving great results for new tasks, but a the same time modern architecture becomes more and more data and resource hungry. You should collect a huge dataset for achieving an acceptable performance of your model. Few-shot learning technique aims to learn a model to recognize unseen classes during training with limited labeled examples.
We want to discuss what few-shot learning is, how to do it right, what kind of problems it can solve and how we use it in our projects.
In this talk, we will deeply look at the few-shot learning approaches. Investigate different architectures and datasets. Figure out when the few-shot learning works and when does not. In the end, you should be able to understand all landscape of few-shot learning field and could continue your journey in this area.
Speaker: Kyryl Truskovskyi.
Presentation language: Russian.
Date and time: September 24th, 8:00-9:30 pm.
Location: Times, room 204.
Videos from previous seminars are available at http://bit.ly/MLJBSeminars