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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/AnguloFlorian/jazz-arpeggiator
https://github.com/Anjaney1999/image-captioning-seqgan
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
https://github.com/AnjieCheng/InstaNAS
https://github.com/AnjieZheng/InstaNAS
https://github.com/Anonnoname/G2GT_2
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
https://github.com/AnzorGozalishvili/embedrank_serving
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings
https://github.com/AnzorGozalishvili/unsupervised_keyword_extraction
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings
https://github.com/ArealTeamM2AIC/Remote-Sensing-Image
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/Arghyadeep/Image-Colorization-using-CNN
https://github.com/ArvinZhuang/BiTAG
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/B1ueber2y/NDiv
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ResT
https://github.com/BerenLuthien/HyperColumns_ImageColorization
https://github.com/Bingwen-Hu/DRIT
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/BirgerMoell/SwedishLLMBenchmark
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/BonaventureR/person-reid
https://github.com/Boyiliee/PONO
Underwater Image Enhancement via Learning Water Type Desensitized Representations
https://github.com/BradyFU/DVG
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition
https://github.com/BunsenFeng/modular_pluralism
https://github.com/Byte7/GANs
https://github.com/COMP6248-Reproducability-Challenge/COMP6248-Reproducability-Challenge-NMN-Drop-for-Reasoning-Over-Text
https://github.com/CV-Reimplementation/ResViT-Reimplementation
ResViT: Residual vision transformers for multi-modal medical image synthesis
https://github.com/CV-Reimplementation/Ucolor-Reimplementation
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/Callifrey/ACGAN-Paddle
https://github.com/Callifrey/Paddle-CIC
https://github.com/Canjie-Luo/Scene-Text-Image-Transformer
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
https://github.com/Chenny0808/ape210k
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Claydon-Wang/Anti-random-pruning
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks
https://github.com/CodingWitcher/reverse-oldification
https://github.com/ContinualAI/avalanche
Gradient based sample selection for online continual learning
https://github.com/DDCM-2025/ddcm-compressed-image-generation
Compressed Image Generation with Denoising Diffusion Codebook Models
https://github.com/DIAGNijmegen/prostateMR_3D-CAD-csPCa
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
https://github.com/DamianStraszak/FairDiverseDPPSampling
https://github.com/Danial-Alh/fast-bleu
Jointly Measuring Diversity and Quality in Text Generation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.