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Data Augmentation Strength, Training Stability, and Clinical Trade-Offs in Transfer Learning for Chest X-ray Pneumonia Classification. [PDF]

Cureus
Dias Will A.
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Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

IEEE International Conference on Computer Vision, 2022
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator. Despite their promising results, such paradigm is computationally expensive.
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Weixian Lei, Yuchao Gu, W. Hsu, Ying Shan, Xiaohu Qie, Mike Zheng Shou   +8 more
semanticscholar   +1 more source

Otter: A Multi-Modal Model With In-Context Instruction Tuning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Recent advances in Large Multimodal Models (LMMs) have unveiled great potential as visual assistants. However, most existing works focus on responding to individual instructions or using previous dialogues for contextual understanding.
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, Ziwei Liu   +5 more
semanticscholar   +1 more source

In Tune or Out of Tune

Journal of Research in Music Education, 2015
We studied music majors’ perception of intonation in accompanied solo performances of trumpet, violin, and voice. We were interested in whether listeners would judge pitch deviations of equal magnitude in the three solo performances as equivalent in intonation.
Geringer, John M., MacLeod, Rebecca B., Sasanfar, Justine K.   +2 more
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To tune or not to tune?: A monitoring procedure to decide

Automatica, 1992
Abstract In this paper we address the practical problem of providing a monitoring procedure to decide when to switch on the parameter update law for an adaptive controller operating in closed loop with a stable plant. Specifically, we introduce a sensitivity-based test to know at any given instant if starting adaptation will improve, or otherwise ...
Romeo Ortega, Gerardo Escobar, Francisco Garcia   +2 more
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning

IEEE Transactions on Audio, Speech, and Language Processing, 2023
Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information while acquiring new knowledge for achieving satisfactory performance in downstream tasks.
Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, Yue Zhang   +5 more
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What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

International Conference on Learning Representations, 2023
Instruction tuning is a standard technique employed to align large language models to end tasks and user preferences after the initial pretraining phase.
Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, Junxian He   +4 more
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Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Robotics
Recent vision-language-action models (VLAs) build upon pretrained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language following ability, and semantic generalization.
Moo Jin Kim, Chelsea Finn, Percy Liang
semanticscholar   +1 more source

Tuning out, tuning in

Early Years Educator, 2014
Interactions between babies and parents provide the foundations for communication and language. We explore what happens to language when adults and children fail to ‘tune into’ each other early in life.
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