Papers with Code 아카이브

NLP based Person Retrival

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/HareeshBahuleyan/tf-var-attention

공식 코드

Variational Attention for Sequence-to-Sequence Models

https://github.com/Hatmm/PED-DETR-for-Pedestrian-Detection

공식 코드

DETR for Crowd Pedestrian Detection

https://github.com/Heidelberg-NLP/SRL-S2S

공식 코드

Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling

https://github.com/HeranYang/Hyper-GAN

공식 코드

A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation

https://github.com/Herrccc/DR-TANet

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DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection

https://github.com/HiLab-git/SSL4MIS

공식 코드

Triple-View Feature Learning for Medical Image Segmentation

https://github.com/HoangTrinh/EDMS

공식 코드

Image Compression with Encoder-Decoder Matched Semantic Segmentation

https://github.com/Holmes-Alan/dSRVAE

공식 코드

Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder

https://github.com/HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation

공식 코드

Location-aware Upsampling for Semantic Segmentation

https://github.com/HongruCai/SLGR

공식 코드

Exploring Training and Inference Scaling Laws in Generative Retrieval

https://github.com/HongyangGao/gunet

공식 코드

Graph U-Nets

https://github.com/Honlan/DMT

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Disentangled Makeup Transfer with Generative Adversarial Network

https://github.com/HuCaoFighting/Swin-Unet

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Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/Hydragon516/ESA-official

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Robust Lane Detection via Expanded Self Attention

https://github.com/IBM/Graph2Seq

공식 코드

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

https://github.com/IBM/Graph2Tree

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Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem

https://github.com/IBM/terratorch

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TerraTorch: The Geospatial Foundation Models Toolkit

https://github.com/IBM/transition-amr-parser

공식 코드

Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

https://github.com/IDEA-Research/OSX

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One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer

https://github.com/IDEA-Research/detrex

공식 코드

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/IDKiro/sdxs

공식 코드

SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions

https://github.com/IGLICT/NeRFFaceEditing

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NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields

https://github.com/INDElab/rgvae

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Knowledge Generation -- Variational Bayes on Knowledge Graphs

https://github.com/INVOKERer/AdaRevD

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AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring

https://github.com/ISosnovik/nn4topopt

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Neural networks for topology optimization

https://github.com/ISosnovik/top

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Neural networks for topology optimization

https://github.com/IVIPLab/FBSNet

공식 코드

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

https://github.com/IcarusWizard/PixelwiseRegression

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Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form Representation and Differentiable Decoder

https://github.com/ImKeTT/adavae

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AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling

https://github.com/ImperialNLP/BertGen

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BERTGEN: Multi-task Generation through BERT

https://github.com/ImperialNLP/MMT-Delib

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Distilling Translations with Visual Awareness

https://github.com/Indolent-Kawhi/EAGER-LLM

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EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration

https://github.com/InterDigitalInc/HRFAE

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High Resolution Face Age Editing

https://github.com/Isminoula/TextNormSeq2Seq

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Adapting Sequence to Sequence models for Text Normalization in Social Media

https://github.com/JD-AI-Research-NLP/RevCore

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RevCore: Review-augmented Conversational Recommendation

https://github.com/JD-AI-Research-Silicon-Valley/SACN

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End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.