INTERN: A New Learning Paradigm Towards General Vision
@article{Shao2021INTERNAN, title={INTERN: A New Learning Paradigm Towards General Vision}, author={Jing Shao and Siyu Chen and Yangguang Li and Kun Wang and Zhen-fei Yin and Yinan He and Jianing Teng and Qinghong Sun and Mengya Gao and Jihao Liu and Gengshi Huang and Guanglu Song and Yichao Wu and Yuming Huang and Fenggang Liu and Huan Peng and Shuo Qin and Chengyu Wang and Yujie Wang and Conghui He and Ding Liang and Yu Liu and Fengwei Yu and Junjie Yan and Dahua Lin and Xiaogang Wang and Y. Qiao}, journal={ArXiv}, year={2021}, volume={abs/2111.08687} }
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However, down the road, a key challenge awaits us, that is, our capability of meeting rapidly-growing scenario-specific demands is severely limited by the cost of acquiring the commensurate amount of training data. This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a…
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