Labeling with EMC sampling

EMC aims to query points that will induce the greatest change in the current model when labeled and trained on. This focuses labeling on points with the highest expected impact.

EMC techniques involve selecting a specific data point to label and learn from to cause the most significant alteration to the current model’s parameters and predictions. The core idea is to query the point that would impact the maximum change to the model’s parameters if we knew its label. By carefully identifying this particular data point, the EMC method aims to maximize the impact on the model and improve its overall performance. The process involves assessing various factors and analyzing the potential effects of each data point, ultimately choosing the one that is expected to yield the most substantial changes to the model, as depicted in Figure 2.8. The goal is to enhance the model’s accuracy and make it more effective in making predictions.

When...