Self-supervised learning (SSL) can improve performance in generative models by leveraging unlabeled data to learn useful representations before fine-tuning on limited annotated datasets. You can refer to the following key steps:
- Pretraining on Unlabeled Data: Train the model using self-supervised tasks like predicting missing parts of data (e.g., image inpainting or contrastive learning) to learn general features.
- Feature Learning: Use the learned representations from SSL as a foundation for generative tasks, reducing reliance on labeled data.
- Label Prediction: Use SSL tasks like predicting transformations (e.g., rotation prediction) to create pseudo-labels for small datasets.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Self-Supervised Pretraining: Leverages unlabeled data to learn useful features.
- Contrastive Loss: Encourages the model to learn representations that are invariant to augmentations.
- Fine-Tuning: Uses the learned representations as a foundation for generative tasks, reducing reliance on labeled data.
- Data Efficiency: Improves performance on small annotated datasets by pretraining on large unlabeled data.
Hence, by referring to the above, you can improve performance on limited annotated datasets.
