Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

1 code implementation • 29 Apr 2024 • Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang, Yishan Shen, Ke Zhang, Rong Zhou, Eashan Adhikarla, Wenxuan Ye, Yixin Liu, Zhaoming Kong, Kai Zhang, Yilong Yin, Vinod Namboodiri, Brian D. Davison, Jason H. Moore, Yong Chen

Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023.

Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts

no code implementations • 3 Dec 2023 • Eashan Adhikarla, Kai Zhang, Jun Yu, Lichao Sun, John Nicholson, Brian D. Davison

As a result, it raises concerns about the overall robustness of the machine learning techniques for computer vision applications that are deployed publicly for consumers.

Data Augmentation Transfer Learning

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

1 code implementation • 26 May 2023 • Kai Zhang, Jun Yu, Eashan Adhikarla, Rong Zhou, Zhiling Yan, Yixin Liu, Zhengliang Liu, Lifang He, Brian Davison, Xiang Li, Hui Ren, Sunyang Fu, James Zou, Wei Liu, Jing Huang, Chen Chen, Yuyin Zhou, Tianming Liu, Xun Chen, Yong Chen, Quanzheng Li, Hongfang Liu, Lichao Sun

Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.

Image Captioning Medical Visual Question Answering +5

Memory Defense: More Robust Classification via a Memory-Masking Autoencoder

1 code implementation • 5 Feb 2022 • Eashan Adhikarla, Dan Luo, Brian D. Davison

Ideally, a robust classifier would be immune to small variations in input images, and a number of defensive approaches have been created as a result.

Classification Robust classification