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Novel Concepts
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lucidrains/PaLM-jax
https://github.com/lucidrains/PaLM-pytorch
https://github.com/mjhucla/TF-mRNN
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
https://github.com/nkluge-correa/teenytinyllama
https://github.com/rvlopes/gloria
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/salman-h-khan/ZSD_Release
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
https://github.com/xcmax/FewShotWithoutForgetting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.