https://github.com/fantabulous-j/bootswitch
공식 코드Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval
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/fantabulous-j/class
공식 코드Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision
https://github.com/fantabulous-j/self-training-dpr
공식 코드Noisy Self-Training with Synthetic Queries for Dense Retrieval
https://github.com/fantast4ever/knowprefix-tuning
공식 코드KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
https://github.com/farahalshanik/qe
공식 코드Proactive Query Expansion for Streaming Data Using External Source
https://github.com/farewellthree/stan
공식 코드Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
https://github.com/fartashf/vsepp
공식 코드VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/fasterdecoding/rest
공식 코드https://github.com/fate-ubw/raglab
공식 코드RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
https://github.com/fatebreaker/RARE
공식 코드RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
https://github.com/fawazsammani/nlxgpt
공식 코드NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
https://github.com/fawuuu/hwf
공식 코드https://github.com/fawuuu/mirror_spr
공식 코드A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
https://github.com/fcakir/mihash
공식 코드https://github.com/fead1/uhrnet
공식 코드UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern
https://github.com/felixgwu/FastFusionNet
공식 코드https://github.com/felixvogel02/vl-taboo
공식 코드VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language Models
https://github.com/fengranMark/PersonalizedCIR
공식 코드How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
https://github.com/fengranmark/convgqr
공식 코드ConvGQR: Generative Query Reformulation for Conversational Search
https://github.com/fengranmark/convrelexpand
공식 코드Learning to Relate to Previous Turns in Conversational Search
https://github.com/fengranmark/convsdg
공식 코드https://github.com/fengranmark/haconvdr
공식 코드https://github.com/ferchonavarro/shape_aware_segmentation
공식 코드Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
https://github.com/feymanpriv/DOLG
공식 코드DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
https://github.com/feyzaakyurek/rl4f
공식 코드RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
https://github.com/fffffarmer/lsar
공식 코드Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
https://github.com/filipradenovic/cnnimageretrieval-pytorch
공식 코드https://github.com/findzebra/fz-openqa
공식 코드https://github.com/fine68/ucdr2024
공식 코드https://github.com/finger-monkey/Data-Augmentation
공식 코드https://github.com/firojalam/assisting-fact-checking
공식 코드Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document
https://github.com/fiveai/hardest
공식 코드Query-based Hard-Image Retrieval for Object Detection at Test Time
https://github.com/fjiangai/cpts
공식 코드https://github.com/fjssharpsword/ASH
공식 코드Attention-based Saliency Hashing for Ophthalmic Image Retrieval
https://github.com/fjssharpsword/ATH
공식 코드Deep Triplet Hashing Network for Case-based Medical Image Retrieval
https://github.com/fjssharpsword/YNet
공식 코드Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.