Data Labeling - Pareto.AI Blog

High Variance: The model becomes too complex, capturing random fluctuations in the training data instead of learning meaningful patterns. As a result, the model performs well on the training data but fails to generalize to new data.
Limited Data: CNNs require a large and diverse dataset to learn robust representations. When the dataset is small or lacks diversity, the model may overfit by memorizing the limited training examples without capturing the underlying patterns.
Complex Architecture: CNNs with many layers or parameters are more prone to overfitting, especially when the model capacity exceeds the task's complexity. Complex architectures may learn to fit the noise in the training data rather than the underlying relationships.
Inadequate Regularization: Insufficient regularization techniques such as dropout, batch normalization, or weight decay can exacerbate overfitting in CNNs. Regularization methods help prevent the model from memorizing the training data by imposing constraints on the network's parameters.
Early Stopping: Failure to monitor the model's performance during training and stop the training process at the right time can also lead to overfitting. Training for too many epochs may cause the model to overfit the training data.

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