I am training a CNN for medical image classification, but my validation loss starts increasing after just 10 epochs while training loss keeps dropping. I’ve already tried adding Dropout layers (0.5) and L2 regularization. Are there more advanced techniques in 2024 to help a Deep Learning model generalize better when the training data is limited?
3 answers
You should definitely implement Early Stopping. It halts training the moment the validation metric stops improving, saving you from useless epochs.
Since you are working with medical images, the most effective strategy is often Data Augmentation specifically tailored to your domain—think elastic transformations or slight rotations that mimic real-world scan variations. Beyond that, ensure you are using Batch Normalization after your convolutional layers; it smooths the optimization landscape and has a slight regularizing effect. Also, consider using a pre-trained model like ResNet or EfficientNet via Transfer Learning. By freezing the early layers, you leverage features learned from millions of images, which is much more stable than training from scratch.
Have you monitored your learning rate? Sometimes a high learning rate causes the model to jump over the local optima, appearing like it's overfitting.
I actually adjusted the learning rate using a "ReduceLROnPlateau" scheduler, James. It helped a bit, but the gap between training and validation is still significant. Barbara’s point about Transfer Learning is probably my next move. I was worried that ImageNet-trained weights wouldn't apply to X-rays, but I've read that the "edges and textures" learned in early layers are actually quite universal across different types of visual data
Agreed, Michael. Early Stopping is a must-have. I usually combine it with ModelCheckpoint to ensure I keep the weights from the absolute best performing epoch.