My CNN model is performing at 98% accuracy on my training data, but as soon as I run it against the validation set, it drops to 65%. I’ve already tried adding Dropout layers, but it hasn't solved the issue. What are some advanced regularization methods or data augmentation strategies that can help me bridge this generalization gap for image classification?
3 answers
Overfitting usually means your model is memorizing noise rather than learning features. Beyond simple Dropout, you should implement "Batch Normalization" after your convolutional layers; it helps stabilize the learning process and acts as a regularizer. Another powerful technique is "Weight Decay" (L2 Regularization), which penalizes large weights. For data augmentation, don't just do flips and rotations; try "CutMix" or "Mixup," which forces the network to learn more robust decision boundaries by combining multiple images. Also, check your learning rate; if it's too high, you might be overshooting the local minima and getting stuck in a sharp, non-generalizable valley.
Have you looked at your dataset balance? If one class is over-represented, the model will naturally "overfit" to that majority class just to minimize the loss function during the initial epochs.
Try adding more diversity to your training data. Synthetic data generation using GANs is a trending way to fill gaps in your dataset and significantly improve the model's ability to generalize.
I agree with Barbara. GAN-generated data has saved many of my niche medical imaging projects where real-world data was scarce. It’s a very modern solution to the overfitting problem.
Robert, the dataset is fairly balanced, but the images are very high resolution. I suspect the model is picking up on background textures rather than the actual objects. Do you think "Early Stopping" would be effective here, or should I look into using a pre-trained model like ResNet and just fine-tuning the final layers to avoid training from scratch?