I. Weak Supervision
Leveraging extensive unlabeled data? Weak Supervision (WS) provides the initial strategy. This potent approach enables programmatic, scaled creation of noisy labels, effectively channeling domain knowledge into a vast training dataset.
The Programmatic Labeling Pipeline
Write Labeling Functions (LFs)
Here are a few options for rewriting the line, maintaining a similar size and conveying the same meaning: * **Implement domain expertise via functions (keywords, patterns, prompts) that label or decline.** * **Translate domain understanding into functions (keywords, patterns, prompts) to assign or withhold labels.** * **Utilize functions (keywords, patterns, prompts) informed by domain knowledge to decide labels or abstain.** * **Create label-voting functions (keywords, patterns, prompts) incorporating domain knowledge, with abstention allowed.** * **Incorporate domain knowledge through functions (keywords, patterns, prompts) to either label or avoid labeling.**
Run Generative Label Model
Here are a few options, aiming for a similar size and meaning: * **This model gauges LF accuracy & correlation by analyzing agreement, sans ground truth.** * **Without gold labels, this model assesses LF accuracy and inter-LF relationships by analyzing agreement.** * **The model estimates LF accuracy and correlations, leveraging LF agreement patterns and avoiding ground truth.** * **Analyzing LF consensus, this model derives LF accuracy and correlation metrics, independently of ground truth.**
Produce Probabilistic Labels
Here are a few rewritten options, maintaining a similar length and meaning: * This creates a complete training set with "soft" labels, reflecting the model's confidence as percentages (e.g., A:90%, B:10%). * The result is a comprehensive training set using "soft" labels, such as 90% Class A and 10% Class B, representing the model's certainty. * The final output is a full training set utilizing "soft" labels, where confidence is indicated (e.g., 90% A, 10% B). * We'll generate a complete training set using "soft" labels. These labels, like 90% A / 10% B, represent model confidence.
Train Discriminative End Model
Leveraging these probabilistic labels, a strong model (like a Transformer) is trained. This enables generalization beyond LF heuristics, yielding a resilient, high-performing model.
II. Active Learning
Active Learning (AL) offers a distinct approach to the labeling challenge. Rather than indiscriminately labeling vast amounts, AL intelligently selects data for annotation, optimizing model gains and minimizing annotation expenses.
The Query Strategy Explorer
Choose a query strategy family; its central idea shapes the active learner.
III. Generative Methods & Optimal Transport
For data augmentation, handling exceptions, or general data creation, generative methods excel. Optimal Transport (OT) offers a robust, geometric approach for generating realistic synthetic data.
Principled Augmentation with OT
Naive Interpolation (e.g., Mixup)
Averaging data risks generating synthetic points that stray from the actual data distribution.
Wasserstein Barycenters (OT)
OT computes a geometric mean, preserving data structure for authentic, in-distribution samples.
The OT Data-Centric Toolkit
WGANs
Employing Wasserstein distance to enhance GAN stability, producing superior synthetic data.
Domain Adaptation
Here are a few options for rewriting the line, all similar in length and capturing the core meaning: * Matches data distributions, connecting source and target domains. * Maps source data onto the target domain, closing the gap. * Transforms data distributions, bridging the domain difference. * Adapts source distributions to align with a target domain. * Reduces domain discrepancy by matching data distributions.
Coreset Selection
Here are a few options for rewriting the line, all similar in length: * Creates a smaller, key sample of a large dataset for faster model training. * Selects a concise, useful portion of a large dataset for quicker model training. * Generates a compact, representative sample from a large dataset to train models. * Extracts a smaller, focused dataset from a larger one to speed up training.
IV. The Unified Framework
**Here are a few options, maintaining a similar length and tone:** * **Synergy unlocks the greatest potential.** Consult this web guide to tailor your data-building approach effectively. * **Integration yields optimal results.** Explore this online guide to select the right data strategy for you. * **Maximize impact through collaboration.** This web resource will help you choose your ideal data-building method.
Recommended Strategy:
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