Implications of continual learning for lesion of the skin categorizing and detection

Development of techniques that can gradually learn from new samples is now important due to the growing number of data. Training a network with new data poses challenges, including the phenomenon known as "catastrophic forgetting." This occurs when a network's performance on previously learned data drops as it tries to learn from new instances. To address the issue of catastrophic forgetting, the article includes a powerful approach known as experience replay. Investigations are conducted on different datasets to analyze the impact of catastrophic forgetting. The technique is compared against various approaches, including individual training, mutual training, and fine-tuning. When compared to the fine-tuning approach, the model using experience replay retains knowledge from the previous dataset, leading to a 13% increase in accuracy.