https://github.com/google/tim-gan
공식 코드Text as Neural Operator: Image Manipulation by Text Instruction
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/google/unisim
공식 코드https://github.com/goukoutaki/FSDH
공식 코드https://github.com/gouqi666/rast
공식 코드Diversify Question Generation with Retrieval-Augmented Style Transfer
https://github.com/gowitheflow-1998/la-ser-cubed
공식 코드Length is a Curse and a Blessing for Document-level Semantics
https://github.com/gowitheflow-1998/rar-b
공식 코드https://github.com/gpantaz/vl_mamba
공식 코드https://github.com/gpt-laboratory/rag-llm-development-guidebook-from-pdfs
공식 코드Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
https://github.com/gr8joo/IIAE
공식 코드Variational Interaction Information Maximization for Cross-domain Disentanglement
https://github.com/graph-com/subgraphrag
공식 코드https://github.com/graphrag-bench/graphrag-benchmark
공식 코드When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
https://github.com/gregor-ge/babel-imagenet
공식 코드Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
https://github.com/greshake/llm-security
공식 코드https://github.com/greshake/lm-safety
공식 코드https://github.com/grill-lab/codec
공식 코드https://github.com/grill-lab/oat
공식 코드https://github.com/grill-lab/vilt
공식 코드https://github.com/gsidiropoulos/dense-retrieval-against-misspellings
공식 코드Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
https://github.com/gsidiropoulos/hybrid_retrieval_for_efficient_qa
공식 코드Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
https://github.com/gsidiropoulos/passage_retrieval_for_spoken_qa
공식 코드On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering
https://github.com/gsidiropoulos/typo-robust-multi-positive-dr
공식 코드Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning
https://github.com/gsyfate/knnlm-limits
공식 코드https://github.com/gtolias/mom
공식 코드Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
https://github.com/gtolias/tma
공식 코드Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower
https://github.com/guanrunwei/findvehicle
공식 코드https://github.com/guanrunwei/vehiclefinder-ctim
공식 코드https://github.com/guest400123064/distill-retriever
공식 코드https://github.com/gugas81/pr-dad
공식 코드https://github.com/guglielmof/ecir2023-qpp
공식 코드Query Performance Prediction for Neural IR: Are We There Yet?
https://github.com/guilhermemr04/scaling-zero-shot-retrieval
공식 코드No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
https://github.com/guilk/kat
공식 코드KAT: A Knowledge Augmented Transformer for Vision-and-Language
https://github.com/gunny97/DRAFT
공식 코드DRAFT: Dense Retrieval Augmented Few-shot Topic classifier Framework
https://github.com/guoqianyu-alberta/rankdnn
공식 코드https://github.com/guoshengcv/cacl
공식 코드Cross-Architecture Self-supervised Video Representation Learning
https://github.com/guotong1988/Semantic-Tree-Search
공식 코드Revisiting Semantic Representation and Tree Search for Similar Question Retrieval
https://github.com/guoyang9/unifer
공식 코드A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.