I hope you had a successful start to the new year, as did AI and deep learning research. In this edition of Ahead of AI #5, I wanted to showcase recent advancements in computer vision rather than simply covering the increasing popularity of large language models. This newsletter aims to revive ideas and take convolutional neural networks to new heights.
One interesting approach that I have come across for Active Learning is Label Dispersion. It's a good way of quantifying model uncertainity. TL;DR- Have a model predict an input's class a bunch of times. If it gets different outputs each time, your model is unsure. Turns out this works a lot better than using confidence.
The original paper introduced this idea in their paper- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning- https://arxiv.org/abs/2107.14707
This idea works for classification, but I've had success expanding it for regression as well. The process is simple- use an ensemble of diverse models, and their spread is the uncertainity of your prediction. You can take it a step further, and use probabilistic models + multiple inferences for more thoroughness.
Very clear explanations throughout the article. Thank you
One interesting approach that I have come across for Active Learning is Label Dispersion. It's a good way of quantifying model uncertainity. TL;DR- Have a model predict an input's class a bunch of times. If it gets different outputs each time, your model is unsure. Turns out this works a lot better than using confidence.
The original paper introduced this idea in their paper- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning- https://arxiv.org/abs/2107.14707
My breakdown of the paper- https://medium.com/mlearning-ai/evaluating-label-dispersion-is-it-the-best-metric-for-evaluating-model-uncertainty-e4a2b52c7fa1
This idea works for classification, but I've had success expanding it for regression as well. The process is simple- use an ensemble of diverse models, and their spread is the uncertainity of your prediction. You can take it a step further, and use probabilistic models + multiple inferences for more thoroughness.