Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/facebookresearch/GPU-DPF
공식 코드GPU-based Private Information Retrieval for On-Device Machine Learning Inference
https://github.com/facebookresearch/LASER
공식 코드Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/facebookresearch/PAQ
공식 코드PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
https://github.com/facebookresearch/active_indexing
공식 코드https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/comet_memory_dialog
공식 코드Navigating Connected Memories with a Task-oriented Dialog System
https://github.com/facebookresearch/concurrentqa
공식 코드Reasoning over Public and Private Data in Retrieval-Based Systems
https://github.com/facebookresearch/contriever
공식 코드Unsupervised Dense Information Retrieval with Contrastive Learning
https://github.com/facebookresearch/distributed-faiss
공식 코드The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
https://github.com/facebookresearch/dp_rdm
공식 코드DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
https://github.com/facebookresearch/dpr-scale
공식 코드https://github.com/facebookresearch/dpr-scale/tree/main/spar
공식 코드Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
https://github.com/facebookresearch/imagebind
공식 코드https://github.com/facebookresearch/imu2clip
공식 코드IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
https://github.com/facebookresearch/light
공식 코드https://github.com/facebookresearch/mavil
공식 코드https://github.com/facebookresearch/meru
공식 코드https://github.com/facebookresearch/multicontrievers-analysis
공식 코드MultiContrievers: Analysis of Dense Retrieval Representations
https://github.com/facebookresearch/multigrain
공식 코드MultiGrain: a unified image embedding for classes and instances
https://github.com/facebookresearch/multihop_dense_retrieval
공식 코드Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
https://github.com/facebookresearch/npm
공식 코드https://github.com/facebookresearch/romqa
공식 코드RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering
https://github.com/facebookresearch/seal
공식 코드Autoregressive Search Engines: Generating Substrings as Document Identifiers
https://github.com/facebookresearch/sphere
공식 코드The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
https://github.com/facebookresearch/supervised-hashing-baselines
공식 코드https://github.com/facebookresearch/tart
공식 코드https://github.com/facebookresearch/timelineqa
공식 코드TimelineQA: A Benchmark for Question Answering over Timelines
https://github.com/facebookresearch/vilbert-multi-task
공식 코드12-in-1: Multi-Task Vision and Language Representation Learning
https://github.com/fahadshamshad/prGAN
공식 코드Robust Compressive Phase Retrieval via Deep Generative Priors
https://github.com/faisal-iut/linkPrediction
공식 코드https://github.com/faisaltareque/XL-HeadTags
공식 코드https://github.com/fandulu/IHDA
공식 코드Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
https://github.com/faneshion/HiNT
공식 코드https://github.com/fangkaipeng/pros
공식 코드ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
https://github.com/fangyihao/hgot
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.