2021

INTERN: A New Learning Paradigm Towards General Vision

Shao, Jing, Chen, Siyu, Li, Yangguang et al.

Understand

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 a commensurate amount of training data.
  • This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a new model for each new scenario, based on a large quantity of well-annotated data and commonly from scratch.
  • In tackling this fundamental problem, we move beyond and develop a new learning paradigm named INTERN.

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