Papers with Code 아카이브
Diversity
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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/researchmm/NEAS
공식 코드One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
https://github.com/researchmm/generate-it
공식 코드https://github.com/resibots/2020_gaier_gecco
공식 코드https://github.com/resibots/2020_mouret_gecco
공식 코드https://github.com/resibots/pymap_elites
공식 코드https://github.com/resibots/robot_dart
공식 코드https://github.com/resibots/vassiliades_2018_gecco
공식 코드Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
https://github.com/resilient-swarms/argos-sferes
공식 코드QED: using Quality-Environment-Diversity to evolve resilient robot swarms
https://github.com/retkowsky/florence-2
공식 코드Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
https://github.com/rewire-online/edos
공식 코드SemEval-2023 Task 10: Explainable Detection of Online Sexism
https://github.com/reyllama/mixdl
공식 코드Few-shot Image Generation with Mixup-based Distance Learning
https://github.com/riacd/cpdiffusion-ss
공식 코드Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion
https://github.com/riccotti/ece
공식 코드https://github.com/richarizardd/self-supervised-vit-path
공식 코드Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
https://github.com/richzhang/colorization
공식 코드https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
공식 코드Improving Neural Conversational Models with Entropy-Based Data Filtering
https://github.com/ricsinaruto/dialog-eval
공식 코드Improving Neural Conversational Models with Entropy-Based Data Filtering
https://github.com/rimchang/Rsblur
공식 코드https://github.com/rio98/hybrid-representation-enhanced-bayesian-active-learning
공식 코드https://github.com/rish-16/rna-backbone-design
공식 코드RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
https://github.com/ritaranx/clingen
공식 코드https://github.com/ritual-uh/da_ner
공식 코드Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
https://github.com/ritual-uh/roast-absa
공식 코드ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
https://github.com/rizwanqureshi123/brain-tumor-synthetic-data
공식 코드Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
https://github.com/robbycostales/diva
공식 코드Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
https://github.com/robincugny/autoxai
공식 코드Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
https://github.com/robinwangsd/diffusion-with-multi-modal-priors
공식 코드https://github.com/robot-learning-freiburg/CoDEPS
공식 코드CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
https://github.com/rohban-lab/rodeo
공식 코드RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
https://github.com/rohitgandikota/sliderspaceweb
공식 코드SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
https://github.com/roholazandie/topical_language_generation
공식 코드https://github.com/roimehrez/DDIS
공식 코드https://github.com/rolnicklab/ami-dataset
공식 코드https://github.com/rongfei-chen/VCAN
공식 코드Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
https://github.com/rongyaofang/puma
공식 코드PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.