DeepSeek addresses class imbalance in training data through a combination of resampling techniques, loss function adjustments, and data augmentation strategies. These methods ensure the model doesn’t become biased toward majority classes while maintaining its ability to generalize. The approach is tailored to the specific dataset and problem type, balancing computational efficiency with performance.
One primary method is resampling, which includes oversampling minority classes and undersampling majority classes. For example, if a dataset has 1,000 examples of Class A but only 50 of Class B, DeepSeek might duplicate or synthesize new instances of Class B using techniques like SMOTE (Synthetic Minority Over-sampling Technique). Conversely, it might randomly remove samples from Class A to reduce skew. In practice, this is often applied dynamically during training—such as using weighted random sampling in data loaders—to ensure each batch contains a balanced mix of classes. For time-series or text data, domain-specific augmentation (like perturbing timestamps or paraphrasing sentences) might be used instead of simple duplication to avoid overfitting.
Another key strategy is modifying the loss function to penalize misclassifications of minority classes more heavily. For instance, Class B might receive a higher weight in the loss calculation, forcing the model to prioritize learning its patterns. In segmentation or detection tasks, focal loss—which reduces the impact of well-classified majority classes—could be employed. DeepSeek might also combine this with architecture adjustments, such as adding auxiliary output layers to reinforce learning for underrepresented classes. Additionally, evaluation metrics like F1-score or AUC-ROC are prioritized over accuracy to better reflect performance on imbalanced data. These techniques are often validated through ablation studies to isolate their impact before full-scale training.