What Is Deep Active Learning: Challenges and Applications

According to NVIDIA, if humans were to label the data for a 100-car fleet driving for eight hours a day, they would require more than 1 million labellers. It takes autonomous vehicles nearly 11 billion miles of driving to perform just 20% better than a human. Real-world problems that machine learning models encounter come with uncertainties and deficiencies. So, keeping the model updated, in other words, making the model smarter even with incoming unknown data is a challenge. This is where Active learning (AL) comes into the picture.