Single-Task Learning (STL)

Single-Task Learning (STL) is the regular machine learning that trains one task on one dataset.

Definition

In CLArena, single-task learning is specifically designed for classification problems and follows the formal definition below.

Definition 1 (Single-Task Learning Classification) Given:

An initialized neural network model \(f\)
- A shared backbone network \(B\)
- An output head
Training data: \(\mathcal{D}_{\text{train}}=\{(\mathbf{x}_i,y_i)\}_{i=1}^{N} \in (\mathcal{X},\mathcal{Y})\)
Validation data: \(\mathcal{D}_{\text{val}}\in (\mathcal{X},\mathcal{Y})\)
Test data: \(\mathcal{D}_{\text{test}} \in (\mathcal{X},\mathcal{Y})\)

Objective: Develop an algorithm that trains the model \(f\) to perform well on test dataset \(\mathcal{D}_{\text{test}}\).

CLArena supports the following experiment and evaluation pipelines for single-task learning:

Single-Task Learning Experiment: The experiment for training and evaluating single-task learning models. See Single-Task Learning Experiment.
Single-Task Learning Evaluation: The evaluation phase for assessing the performance of the trained single-task learning models. See Save and Evaluate Model.