Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ErikEkstedt/TurnGPT
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/EuphoriaYan/ChatRobot-For-Keras2
https://github.com/Evgeneus/Graph-Domain-Adaptaion
https://github.com/ExplainableML/cosmos
FLAIR: VLM with Fine-grained Language-informed Image Representations
https://github.com/FEIfei-coder/circle-loss-for-reid
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/Faceplugin-ltd/FaceRecognition-Android
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/FenQQQ/Fusion-in-decoder
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/FreddeFrallan/Multilingual-CLIP
https://github.com/GaetanBaert/OMR_deep
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
https://github.com/GauriJagatap/invimaging-deeppriors
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
https://github.com/GauriJagatap/model-copram
https://github.com/Gbrtenorio/Gbrtenorio.github.io
https://github.com/GeneralCoder365/agreemate
https://github.com/Georgetown-IR-Lab/curricula-neural-ir
https://github.com/GerardLiu96/FECNet
https://github.com/Gibbsdavidl/gssnng
A Family of Rank Similarity Measures based on Maximized Effectiveness Difference
https://github.com/GiuliaLanzillotta/exercises
https://github.com/Godofnothing/optimal_transport_problem
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
https://github.com/Gorov/three_player_for_emnlp
https://github.com/Grecil/Corrective-RAG
https://github.com/GuHongyang/Person-ReID-Pytorch
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/Guzpenha/MANtIS
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset
https://github.com/HKUST-KnowComp/MnemonicReader
https://github.com/HSISeg/HSISeg
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
https://github.com/HajimeTaira/InLoc_demo
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/Hannibal046/nanoDPR
https://github.com/HaoZeSun2016/HNSW-HAMMING
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/Hermes777/mmfashion
MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
https://github.com/HobbitLong/CMC
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
https://github.com/HoganZhang/Person_reID_baseline_pytorch
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/HolmesShuan/K-Nearest-Neighbors-Hashing
https://github.com/HuangXiaoquan127/Google-Landmarks-Retrieval-and-Recognition-2019-19h-Place-Solution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.