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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. It involves selecting unlabeled data items to the label to best improve an existing classifier. Active learning is an ongoing active research sub-domain within deep learning space that is developed to help models make more accurate decisions.
Active Learning aims to select the most useful samples from the unlabeled dataset and pass it on to the annotators for labelling. However, active learning algorithms have struggled with high-dimensional data. Therefore, attention is now shifting towards filling the voids of active learning with the advantages of deep learning.
In an exclusive survey conducted by the researchers at Northwest University, the state of deep active learning(DAL) was investigated. The researchers discussed various factors hindering the …
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