Self-supervised learning framework for efficient classification of endoscopic images using pretext tasks
Fig 5
Training and validation loss per epoch for different pretext task combinations.
Subplot (a) shows Scenario 1, where training loss decreases rapidly and stabilizes, with test loss following a similar trend. Subplot (b) depicts Scenario 2, where both training and test loss decrease rapidly initially and then gradually, with training loss generally lower than test loss. Subplot (c) illustrates Scenario 3, with training loss decreasing quickly and stabilizing, while test loss follows a similar trend but remains slightly higher. Subplot (d) represents Scenario 4, where training loss decreases rapidly and stabilizes, with test loss following a similar pattern but with higher initial values.