GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
This addresses the problem of data scarcity and annotation costs in machine learning, offering a novel end-to-end approach, though it is incremental as it builds on existing active learning and text-to-image techniques.
The paper tackles the challenge of active learning's dependence on limited labeled data by integrating zero-shot text-to-image synthesis to generate informative synthetic samples from text descriptions, resulting in consistent and significant improvements over traditional methods.
Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model training. However, a significant challenge of AL is its dependence on the limited labeled data samples and data distribution, resulting in limited performance. To address this limitation, this paper integrates the zero-shot text-to-image (T2I) synthesis and active learning by designing a novel framework that can efficiently train a machine learning (ML) model sorely using the text description. Specifically, we leverage the AL criteria to optimize the text inputs for generating more informative and diverse data samples, annotated by the pseudo-label crafted from text, then served as a synthetic dataset for active learning. This approach reduces the cost of data collection and annotation while increasing the efficiency of model training by providing informative training samples, enabling a novel end-to-end ML task from text description to vision models. Through comprehensive evaluations, our framework demonstrates consistent and significant improvements over traditional AL methods.