https://github.com/google/mcafp
공식 코드Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
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/gudovskiy/beta-fridge
공식 코드https://github.com/hferdous/TimeGraph
공식 코드TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery
https://github.com/hiroshikera/transformer-groebner
공식 코드https://github.com/hkunlp/progen
공식 코드ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
https://github.com/howiehwong/unigen
공식 코드UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models
https://github.com/ika-rwth-aachen/multicorrupt
공식 코드https://github.com/j-bagel/treecut-math
공식 코드TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM Hallucination Evaluation
https://github.com/jbesty/irep_2022_closing_the_loop
공식 코드Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
https://github.com/jianghaiscu/realsh
공식 코드Supervised Homography Learning with Realistic Dataset Generation
https://github.com/jiaweilian/benchmarking_physical_attack
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jiaweilian/padetbench
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jschuetzke/synthetic-spectra-generation
공식 코드A universal synthetic dataset for machine learning on spectroscopic data
https://github.com/junchengli1/Sim-Suction-API
공식 코드Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark
https://github.com/junchengli1/sim-mees
공식 코드https://github.com/klusai/tinyfabulist
공식 코드TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models
https://github.com/lamm-mit/Cephalo
공식 코드Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
https://github.com/lamm-mit/Cephalo-Phi-3-MoE
공식 코드Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
https://github.com/lamsoma/auto_nli
공식 코드Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples
https://github.com/liulu112601/Gated-Propagation-Net
공식 코드https://github.com/liutiedong/goat
공식 코드Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
https://github.com/lnikioffic/track-anything-annotate
공식 코드Track Anything Annotate: Video annotation and dataset generation of computer vision models
https://github.com/logic-factory/ace
공식 코드https://github.com/luisrui/seeing-ai-system
공식 코드https://github.com/luka-group/execution-grounded-reasoning
공식 코드https://github.com/lywww/graphcleaner
공식 코드GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
https://github.com/malrabeiah/VABT
공식 코드ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions
https://github.com/manestay/paxqa
공식 코드PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale
https://github.com/matanat/ivd-calibnn
공식 코드https://github.com/matheus-schmitz/avaxtar
공식 코드https://github.com/mazqtpopx/cranfield-synthetic-drone-detection
공식 코드Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data
https://github.com/megvii-research/realflow
공식 코드RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
https://github.com/meyerls/pegasus
공식 코드https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
https://github.com/mlpgroup/e-gen
공식 코드E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions
https://github.com/morse-benchmark/morse-500-code
공식 코드MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.