https://github.com/masud99r/code-intent
공식 코드Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
- 보존된 평가 기록
- 19
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/masyagin1998/robin
공식 코드ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents
https://github.com/matanabudy/mdl-hn
공식 코드https://github.com/matanbt/gaslite
공식 코드GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search
https://github.com/mathetake/intergo
공식 코드https://github.com/mathieulagrange/lostanlen2020jasmp
공식 코드https://github.com/matt-bendel/rcgan
공식 코드A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/mattiaspaul/TernaryNet
공식 코드https://github.com/mauritsbleeker/ecir-2022-reproducibility-bleeker
공식 코드Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?
https://github.com/mauritsbleeker/reducing-predictive-feature-suppression
공식 코드Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval
https://github.com/mausamsion/mfbe
공식 코드MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval
https://github.com/maxwell1447/fairseq
공식 코드Towards Example-Based NMT with Multi-Levenshtein Transformers
https://github.com/maxxu05/rebar
공식 코드REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
https://github.com/mayank31398/VRAG
공식 코드Variational Learning for Unsupervised Knowledge Grounded Dialogs
https://github.com/mayhugotong/gentkg
공식 코드GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
https://github.com/mayo-radiology-informatics-lab/conflare
공식 코드https://github.com/mayu-ot/oc-cost
공식 코드https://github.com/mayug/0-shot-llm-vision
공식 코드Do Vision and Language Encoders Represent the World Similarly?
https://github.com/mbzuai-nlp/fire
공식 코드FIRE: Fact-checking with Iterative Retrieval and Verification
https://github.com/mcahny/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/mcc-wh/csa
공식 코드Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
https://github.com/mcc-wh/ssp
공식 코드Structure Similarity Preservation Learning for Asymmetric Image Retrieval
https://github.com/mcc-wh/token
공식 코드https://github.com/mcg-nju/sorce
공식 코드https://github.com/mcgill-nlp/imagecode
공식 코드https://github.com/mcgill-nlp/retriever-lm-reasoning
공식 코드https://github.com/mchancan/deepseqslam
공식 코드DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
https://github.com/mchong6/RetrieveInStyle
공식 코드Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
https://github.com/mcr-peft/ex-mcr
공식 코드https://github.com/mczhuge/Kaleido-BERT
공식 코드Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
https://github.com/mdda/worldtree_corpus
공식 코드https://github.com/mdenizturkmen/ecir2023
공식 코드New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches
https://github.com/mdmustafizurrahman/An-Information-Retrieval-Approach-to-Building-Datasets-for-Hate-Speech-Detection
공식 코드An Information Retrieval Approach to Building Datasets for Hate Speech Detection
https://github.com/mdw771/probe_position_correction_w_ptychonn
공식 코드Predicting ptychography probe positions using single-shot phase retrieval neural network
https://github.com/medarc-ai/fmri-reconstruction-nsd
공식 코드Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
https://github.com/medarc-ai/mindeyev2
공식 코드MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.