Guide for active learning in computer vision

Jan 19, 2023 · chatgpt ·

Active learning in computer vision is a powerful technique for improving the accuracy of machine learning models. It involves selecting the most informative training data to train the model on, rather than randomly sampling from a large dataset. Active learning helps to reduce the amount of data required to train a model and can improve accuracy by up to 40%.

Active learning works by focusing on samples that the model finds difficult to classify correctly. An algorithm called query strategy selects such data points and presents them to a human expert who can provide labeled data. This labeled data is used to make the model more accurate and reduce its error rate.

To apply active learning to computer vision, images must first be annotated with labels corresponding to the desired object classes. A query strategy is then used to select the most important images for labeling. Examples of query strategies include uncertainty sampling, where the model makes predictions with relatively low confidence, and margin sampling, where the model selects data points which lie close to the decision boundary of classifier.

Once the data has been labeled, it is used as input to a machine learning model. The model is then trained on the newly labeled data and tested against a validation set to assess its accuracy. As the model trains, it becomes increasingly accurate and gains better understanding of the underlying concepts.

In summary, active learning in computer vision is an effective technique for improving the accuracy of machine learning models. It involves selecting and labeling the most important data points to feed into the model and testing the model's accuracy against a validation set. By using this method, a machine learning model can become increasingly accurate and gain better understanding of the underlying concepts.