6 Applying Active Learning to Different Machine Learning Tasks

 

This chapter covers

  • Implementing methods for calculating Uncertainty and Diversity for Computer Vision tasks like object detection and semantic segmentation.
  • Implementing methods for calculating Uncertainty and Diversity for Natural Language Processing tasks like Sequence Labeling and Text Generation.
  • Understanding methods for calculating Uncertainty and Diversity for other Machine Learning tasks in Speech, Video and Information Retrieval.
  • Deciding on the right number of number of samples per iteration for Advanced Active Learning techniques.

The last three chapters the examples and algorithms focused on document-level or image-level predictions. In this chapter, you will learn how the same principles of Uncertainty Sampling and Diversity Sampling can be straightforwardly applied to more complicated Computer Vision tasks like object detection and semantic segmentation (pixel labeling) and more complicated Natural Language Processing tasks like Named Entity Recognition and Natural Language Generation. The general principles are the same and in many cases there is no change needed at all. The biggest differences will be in how you decide to sample the items selected by Active Learning and that will depend on the real-world problem that you are trying to solve.

6.1       Applying Active Learning to object detection

6.1.1   Accuracy for object detection: Label Confidence and Localization

6.1.2   Uncertainty Sampling for Label Confidence and Localization in object detection

6.1.3   Diversity Sampling for Label Confidence and Localization in object detection

6.1.4   Active Transfer Learning for Object Detection

6.1.5   Set a low object detection threshold to avoid perpetuating bias

6.1.6   Create training data samples for Representative Sampling that are similar to your predictions

6.1.7   Sample randomly and consider some image-level sampling

6.1.8   Consider tighter masks when using polygons

6.2       Applying Active Learning to semantic segmentation

6.2.1   Accuracy for semantic segmentation

6.2.2   Uncertainty Sampling for semantic segmentation

6.2.3   Diversity Sampling for Semantic Segmentation

6.2.4   Active Transfer Learning for Semantic Segmentation

6.2.5   Sample randomly and consider some image-level sampling

6.3       Applying Active Learning to Sequence Labeling

6.3.1   Accuracy for Sequence Labeling

6.3.2   Uncertainty Sampling for Sequence Labeling

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