In this post, we will cover a novel form of channel attention called the Style Recalibration Module (SRM), an extension of the popular TPAMI paper: Squeeze-and-Excitation Networks.
In this post, we describe the anatomy of how most Deep Reinforcement Learning algorithms work. We also cover the motivation to use RL over standard machine learning, On-Policy v/s Off-Policy learning, the Exploration-Exploitation Tradeoff, and many more important RL concepts.
In this blog, we will show an example of how to train and generalize Scaled-YOLOv4 on your custom dataset to detect custom objects.
Take a look at this list of top AI artists and explore how each artist integrates AI into their work and respective mediums in this new and exciting era of collaboration.
