Papers with Code 아카이브
Diversity
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Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/WANG-Chaoyue/EvolutionaryGAN
https://github.com/WANG-Chaoyue/EvolutionaryGAN-pytorch
https://github.com/WadhwaniAI/pest-monitoring
https://github.com/WangFengJiee/MiniMaxAD
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/WentseChen/Soft-QMIX
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
https://github.com/Wikidepia/indonesia_dataset
https://github.com/Woodman718/FixCaps
https://github.com/WuChenshen/MeRGAN
https://github.com/XGenerationLab/M-Schema
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/XGenerationLab/XiYan-DateResolver
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/Xephyrum/Agriculturice
https://github.com/Xephyrum/CropsMD
https://github.com/Yash-10/modified_gravity_emulation
https://github.com/YeongHyeon/ConAD
https://github.com/YeongHyeon/ConAD-PyTorch
https://github.com/Yijunmaverick/FlowGrounded-VideoPrediction
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
https://github.com/Yorko/gender-unbiased_BERT-based_pronoun_resolution
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
https://github.com/ZJULearning/diverse_image_synthesis
https://github.com/Zhangyanbo/iResNetLab
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/ZhiluZhang123/neurips_2020_distillation
https://github.com/ZhimingZhou/AM-GAN2
https://github.com/ZikangLeng/IMUGPT
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
https://github.com/Ziyang412/VideoTree
https://github.com/aadharna/UntouchableThunder
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/accettullihuber/nlp-tools
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
https://github.com/acl21/deep-active-learning-pytorch
Towards Robust and Reproducible Active Learning Using Neural Networks
https://github.com/aclyde11/RNNGenerator
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/adaptive-intelligent-robotics/dcg-map-elites
https://github.com/ader47/jittor-jieke-semantic_images_synthesis
https://github.com/adhyasuman/ntms_dropout_analysis
Improving Contextualized Topic Models with Negative Sampling
https://github.com/adithyamurali/TaskGrasp
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
https://github.com/aditya12agd5/divcolor
https://github.com/adymaharana/DeepPath_PyTorch
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.