When faced with a large dataset and few labels, supervised learning alone is insufficient. My approach would involve exploring semi-supervised or self-supervised learning techniques.
First, I'd clarify the data modality (e.g., images, text) to tailor transformations and feature extraction.
Then, I'd consider strategies like:
- Semi-Supervised Learning: Utilize the few labeled examples to train an initial model. This model can then be used to predict labels for the unlabeled data (pseudo-labeling). These pseudo-labels, though potentially noisy, can augment the training set for further model refinement. Techniques like consistency regularization can also be employed, where the model is trained to produce similar outputs for perturbed versions of unlabeled data.
- Self-Supervised Learning: Leverage the inherent structure of the unlabeled data to create supervisory signals. For instance, in image data, pretext tasks like predicting image rotations or filling in missing patches can train powerful feature representations. These pre-trained models can then be fine-tuned on the small labeled subset for the downstream task.
- Active Learning: Strategically select the most informative unlabeled data points for manual labeling. This prioritizes human annotation effort on samples that are expected to yield the greatest improvement in model performance, making the labeling process more efficient.
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