Learning with Limited Labels - ODSC West 2020

Explore cutting-edge techniques for machine learning with limited labeled data in this 40-minute conference talk from ODSC West 2020. Delve into domain adaptation, low-shot learning, and self-supervised learning algorithms that enable transfer of information across multiple domains and recognition of novel categories with few-shot samples. Discover how these approaches allow learning systems to automatically adapt to real-world variations and new environmental conditions. Examine specific topics such as adversarial multiple source domain adaptation, multi-source distilling domain adaptation, learning invariant risks and representations for domain transfer, compositional few-shot recognition with primitive discovery and enhancing, distant-domain few-shot recognition with mid-level patterns, and generalized zero-shot learning with dual adversarial networks. Gain insights into overcoming the challenges of obtaining large-scale labeled datasets and learn strategies to develop more efficient and adaptable machine learning models.