https://github.com/vinayak19th/ARCNN-keras
Compression Artifacts Reduction by a Deep Convolutional Network
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Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)
Surveys on entity resolution:
Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey, 2020.
Barlaug and Gulla: Neural Networks for Entity Matching: A Survey, 2021.
The task of entity resolution is closely related to the task of entity alignment which focuses on matching entities between knowledge bases. The task of entity linking differs from entity resolution as entity linking focuses on identifying entity mentions in free text.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Compression Artifacts Reduction by a Deep Convolutional Network
AutoBlock: A Hands-off Blocking Framework for Entity Matching
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Compression Artifacts Reduction by a Deep Convolutional Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.