Active Learning

This describes how to focus labeling efforts using TensorLeap’s active learning capabilities.

What Is Active Learning and Why It Matters

Active learning is a training strategy where the model actively guides which data should be labeled next, rather than relying on random or exhaustive annotation. In real-world systems, large portions of data are redundant, easy, or already well understood by the model, while a small subset of samples drives most errors and uncertainty.

TensorLeap enables this process by automatically analyzing model representations to identify a diverse and informative subset of unlabeled data to prioritize for labeling. This allows teams to focus labeling resources on under-represented and impactful regions, reducing annotation cost and time.

How TensorLeap Helps Prioritize Labeling and Scene Selection

TensorLeap’s latent space representation powers dataset curation by automatically selecting and suggesting samples from an unlabeled dataset for labeling.

The resulting selection highlights how even when the unlabeled data is highly concentrated and dense (yellow circles), TensorLeap is able to identify samples that expand coverage across the full problem space (blue circles).

By combining active learning principles with TensorLeap’s latent space analysis, dataset curation becomes a fast, data-driven process rather than a manual trial-and-error effort. Instead of relying on intuition or random sampling, teams can systematically prioritize the most impactful unlabeled samples—improving coverage of the problem space, reducing redundancy, and making more efficient use of labeling resources. This results in faster iteration cycles and more reliable performance improvements with fewer labeled samples.

	Manual Approach	With TensorLeap
Sample-Selection Strategy	Hand-picked, Random or intuition-based sampling	Automatic, model-aware, based on learned representations
Data redundancy	High redundancy in labeled data	Diverse samples that expand coverage of the data space
Coverage of edge cases	Difficult to identify, heavily dependent on domain expert	Automatic model specific discovery
Automation level	Requires manual analysis and repeated trial-and-error	Automated dataset curation with minimal user effort
Labeling efficiency	Inefficient use of labeling budget	Focused labeling that maximizes impact per sample
Use of metadata	Heavy reliance on predefined metadata and heuristics	Metadata used as complementary signal alongside model representations
Choosing the amount of samples to label	Arbitrary and often over or undershoot model needs	Can be automatically inferred