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/MindCode-4/code-11/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindCode-4/code-12/tree/main/model-conversion-via
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/MindCode-4/code-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-14/tree/main/SNUH
https://github.com/MindCode-4/code-2/tree/main/prophetnet
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/MindCode-4/code-4/tree/main/rwkv
https://github.com/MindCode-4/code-5/tree/main/tvlt
https://github.com/MindCode-4/code-5/tree/main/umt5
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/MindCode-4/code-5/tree/main/vilt
https://github.com/MindCode-4/code-5/tree/main/x_clip
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindCode-4/code-6/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindCode-4/code-8/tree/main/openfashionclip-vision-and-language
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
https://github.com/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-2/code2/tree/main/pcb
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/MindSpore-paper-code-2/code2/tree/main/pcb_rpp
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/MindSpore-paper-code-3/code2/tree/main/DDAG
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/MindSpore-paper-code-3/code2/tree/main/delf
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/MindSpore-paper-code-3/code8/tree/main/DDAG
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/MindSpore-paper-code-3/code8/tree/main/delf
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/MindSpore-paper-code-3/code9/tree/main/snuh
https://github.com/MindSpore-paper-code-3/code9/tree/main/tinybert
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/MindSpore-scientific-2/code-10/tree/main/MicAugment
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/MindSpore-scientific-2/code-10/tree/main/text-to-music
Music Artist Classification with Convolutional Recurrent Neural Networks
https://github.com/MindSpore-scientific-2/code-14/tree/main/led
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
https://github.com/MindSpore-scientific-2/code-4/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
https://github.com/MindSpore-scientific-2/code-4/tree/main/PR_Product
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
https://github.com/MindSpore-scientific-2/code-4/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-5/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/MindSpore-scientific-2/code-5/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific/code-12/tree/main/AGW
Multilingual Jointly Trained Acoustic and Written Word Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.