Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/prabhakar267/LASER-improved
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/pranavgollamudi/Chatbot
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/predict-idlab/RR-GCN
https://github.com/primeqa/primeqa/blob/xtr/notebooks/ir/dense/xtr_retrieval.ipynb
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
https://github.com/princeton-nlp/SimCSE
https://github.com/princeton-nlp/helmet
https://github.com/princeton-pli/prulong
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads
https://github.com/priscillalui/StackGAN-Stories
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/prithivirajdamodaran/flashrank
https://github.com/probabilists/azula
Diffusion Posterior Sampling for General Noisy Inverse Problems
https://github.com/probcomp/cgpm
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
https://github.com/pshroff04/Dual_Path_CNN
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
https://github.com/pwc-1/Paper-5/tree/main/mistral
https://github.com/pwc-1/Paper-5/tree/main/nllb
NLLB-CLIP -- train performant multilingual image retrieval model on a budget
https://github.com/pwc-1/Paper-8/tree/main/align
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/pwc-1/Paper-8/tree/main/blip
https://github.com/pwc-1/Paper-8/tree/main/clvp
CLIP-Event: Connecting Text and Images with Event Structures
https://github.com/pwc-1/Paper-9/tree/main/1/x_clip
https://github.com/pwc-1/Paper-9/tree/main/2/nllb
NLLB-CLIP -- train performant multilingual image retrieval model on a budget
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/pwc-1/Paper-9/tree/main/5/vilt
https://github.com/pwc-1/Paper-9/tree/main/5/x_clip
ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/pwc-1/Paper-9/tree/main/6/SNUH
https://github.com/pwc-1/Paper-9/tree/main/led
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
https://github.com/qcri/dialectal_arabic_segmenter
https://github.com/qcri/dialectal_arabic_tools
https://github.com/qianjinhao/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/qianlinjun/delf-pytorch
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/qiaozhijian/LPD-Net-Pytorch
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
https://github.com/qihoo360/360zhinao
https://github.com/qingzhiwu/Temporal-Context-Aggregation-Network-Pytorch
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
https://github.com/qiuqiu1602/Pointer_Value_Retrieval
https://github.com/qq456cvb/KdNet-Tensorflow
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
https://github.com/quantumopticslkb/phase_retrieval
Optimization of phase retrieval in the Fresnel domain by the modified Gerchberg-Saxton algorithm
https://github.com/qurator-spk/sbb_binarization
A selectional auto-encoder approach for document image binarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.