Papers with Code 아카이브
Entity Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Uranium-Deng/Steganalysis-StegoRemoval
Destruction of Image Steganography using Generative Adversarial Networks
https://github.com/WenxueCui/LapCSNet
https://github.com/alistairwgillespie/deep_dga_detection
Detecting DGA domains with recurrent neural networks and side information
https://github.com/anhaidgroup/deepmatcher
https://github.com/anhaidgroup/sparkly
https://github.com/ankitf/artifact_reduction_jpeg
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/brandoningli/cs-455-project
https://github.com/c00k1ez/plain-transformers
https://github.com/chatPD-web/ChatPD
https://github.com/cleanzr/dblink-experiments
https://github.com/clovaai/ext_portrait_segmentation
https://github.com/cpuimage/SINet
https://github.com/fadedcosine/pos-guided-neural-text-generation
https://github.com/fminference/flexgen
https://github.com/google/t5patches
https://github.com/griff4692/calibrating-summaries
https://github.com/hieunpd/background-matting
https://github.com/ljyflores/loss-library
https://github.com/makbn/robem
https://github.com/mayurbhangale/Text2Tracks
Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval
https://github.com/mdcnn/mdcnn.github.io
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
https://github.com/megagonlabs/sudowoodo
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation
https://github.com/mibarg/IP-Grouping
https://github.com/ngmarchant/dblink-experiments
https://github.com/ngmarchant/oasis
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling
https://github.com/osmr/imgclsmob
https://github.com/qcri/deepblocker
https://github.com/rotaryhammer/code-autodan
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
https://github.com/royshil/obs-backgroundremoval
https://github.com/ruc-datalab/DADER
https://github.com/ryanxingql/powerqe
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/shuhuayu/dist-sign-momentum
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/stanford-mast/nn_dataflow
A Systematic Approach to Blocking Convolutional Neural Networks
https://github.com/valires/er-evaluation
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
https://github.com/vgudapati/DRLND_Collaboration_Competetion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.