Robuta

Abstract page for arXiv paper 2407.04877: Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for...

M. Amin Farajian, Marco Turchi, Matteo Negri, Marcello Federico. Proceedings of the Second Conference on Machine Translation. 2017.

Abstract page for arXiv paper 1806.04381: Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains

04/25/18 - Domain adaptation is widely used in learning problems lacking labels. Recent researches show that deep adversarial domain adaptati...

02/16/16 - Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data ...

01/09/23 - In an effort to reduce annotation costs in action recognition, unsupervised video domain adaptation methods have been proposed tha...

Background: Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep...

Developing computational methods for single-cell drug response prediction deepens our understanding of tumor heterogeneity and uncovers resistance mechanisms...

Abstract page for arXiv paper 2304.04494v1: Improved Test-Time Adaptation for Domain Generalization

Abstract page for arXiv paper 2306.16660: Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving

Dustin Wright, Isabelle Augenstein. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Shuhao Gu, Yang Feng, Qun Liu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human...

Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private...

Human activity recognition (HAR) has attracted significant attention during recent years due to its critical role in a wide range of applications. Among...

Continual pre-training (CPT) for domain adaptation must balance target-domain gains with stability on the base domain. Existing CPT scaling laws typically...

Abstract page for arXiv paper 1507.07830: Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian

Semantic segmentation and depth estimation tasks are crucial for autonomous driving systems, but obtaining their labels from real-world datasets is costly. To...