https://github.com/3dlg-hcvc/generalizing_shape_retrieval
공식 코드Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects
Papers with Code 아카이브
Dataset Generation
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The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fail to capture rare but essential situations that can pose risks during testing.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/921kiyo/3d-dl
공식 코드Synthetic dataset generation for object-to-model deep learning in industrial applications
https://github.com/AIS-Bonn/synpick
공식 코드SynPick: A Dataset for Dynamic Bin Picking Scene Understanding
https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt
공식 코드Improving Paraphrase Detection with the Adversarial Paraphrasing Task
https://github.com/Amit507017/FedCollabNN
공식 코드Private Dataset Generation Using Privacy Preserving Collaborative Learning
https://github.com/Avinash2468/RackLay
공식 코드https://github.com/BerkeleyAutomation/sd-maskrcnn
공식 코드Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
https://github.com/Daniel-Wu/AfroMNIST
공식 코드Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages
https://github.com/DingfanChen/GAN-Leaks
공식 코드GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
https://github.com/Ezharjan/AutoSceneGen
공식 코드Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner
https://github.com/Genaios/MultiSynFact
공식 코드Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking
https://github.com/GeomaticsCaminosUPM/GeoVisionDataset
공식 코드https://github.com/Guang000/Awesome-Dataset-Distillation
공식 코드Generative Dataset Distillation: Balancing Global Structure and Local Details
https://github.com/HKUNLP/zerogen
공식 코드ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/ICLab4DL/GNNBenchEffectiveness
공식 코드Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
https://github.com/JonasNasimzada/LetsPlay4Emotion
공식 코드Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints
https://github.com/KID-22/CTAR
공식 코드A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
https://github.com/Letian2003/MM_INF
공식 코드Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
https://github.com/Logic-Factory/ACE/blob/master/OpenLS-D/readme.md
공식 코드https://github.com/M4gicT0/autonomous-drone-racing
공식 코드Image Generation for Efficient Neural Network Training in Autonomous Drone Racing
https://github.com/MeetKai/MK-SQuIT
공식 코드MK-SQuIT: Synthesizing Questions using Iterative Template-filling
https://github.com/NVIDIA/DeepLearningExamples
공식 코드A Framework for Large Scale Synthetic Graph Dataset Generation
https://github.com/NeurAI-Lab/biHomE
공식 코드Perceptual Loss for Robust Unsupervised Homography Estimation
https://github.com/Oleffa/LeagueAI
공식 코드https://github.com/PaulSK98/Nerf2Dataset
공식 코드Automating 3D Dataset Generation with Neural Radiance Fields
https://github.com/PedroBarbosa/Synthetic_datasets_generation
공식 코드Semantically Rich Local Dataset Generation for Explainable AI in Genomics
https://github.com/PedroBarbosa/dress
공식 코드Semantically Rich Local Dataset Generation for Explainable AI in Genomics
https://github.com/RickySkywalker/TheoremLlama
공식 코드TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts
https://github.com/SCL-UT/face-manifold
공식 코드Face Manifold: Manifold Learning for Synthetic Face Generation
https://github.com/SamsonYuBaiJian/actionet
공식 코드https://github.com/ZaiqiangWu/RTV
공식 코드Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments
https://github.com/a-nau/synthetic-dataset-generation
공식 코드Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/academiasinicanlplab/locavqg
공식 코드Location-Aware Visual Question Generation with Lightweight Models
https://github.com/acis2021/viewplanningtoolbox
공식 코드Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction
https://github.com/aic-factcheck/multilingual-fact-checking
공식 코드Pipeline and Dataset Generation for Automated Fact-checking in Almost Any Language
https://github.com/aixplain/tts-qa
공식 코드An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.