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An AI developer is fine-tuning a deep learning model for image recognition tasks. During the training process, the model's performance is measured by its accuracy on a separate validation dataset after each training epoch. The model demonstrates consistent improvement in accuracy up to the 100th epoch. However, post-100th epoch, while the training accuracy still improves, the validation accuracy starts to decline. What is the most probable remediation for this divergence in accuracy trends between the training and validation sets?