This describes how to choose samples for labelling within the platform
In many real-world workflows, teams maintain a large pool of unlabeled data, but only a limited labeling budget. Periodically, a small subset of that data is selected for annotation — often based on heuristics, random sampling, or gut feeling.
But not all samples are equally valuable. Labeling redundant, uninformative, or already well-represented data leads to:
Slow improvements in model performance
Wasted annotation effort
Missed edge cases and critical blind spots
The real challenge is deciding what to label — selecting the most informative, high-impact samples from a sea of unlabeled inputs.
Once a model has been evaluated within the platform, Tensorleap enables you to prioritize labeling with a single click. It analyzes the model’s latent space to rank samples from the unlabeled pool based on their potential value to the model.
This means you can:
Focus labeling effort on what matters most
Avoid over-labeling redundant samples
Make measurable progress with each new annotation cycle
You can choose how many samples to retrieve — or let Tensorleap recommend a number based on the current model state.
📸 [Insert screenshot placeholder: prioritized sample selection UI]
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