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/afcruzs/AOEA
공식 코드Self-adaptation of Genetic Operators Through Genetic Programming Techniques
https://github.com/afrisenti-semeval/afrisent-semeval-2023
공식 코드AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
https://github.com/afruehstueck/insetGAN
공식 코드https://github.com/ag2ai/ag2
공식 코드Adaptive In-conversation Team Building for Language Model Agents
https://github.com/agaier/tdomino_ppsn
공식 코드T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective
https://github.com/agemagician/Ankh
공식 코드Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
https://github.com/agentic-learning-ai-lab/procreate-diffusion-public
공식 코드ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation
https://github.com/agosl/Orthogonal_Ensemble_Networks
공식 코드Orthogonal Ensemble Networks for Biomedical Image Segmentation
https://github.com/agostini335/mmvmvae-mimic
공식 코드Unity by Diversity: Improved Representation Learning in Multimodal VAEs
https://github.com/ahxmeds/autosegnet
공식 코드https://github.com/ai-application-and-integration-lab/ridge
공식 코드Relation-Rich Visual Document Generator for Visual Information Extraction
https://github.com/ai4bharat/lahaja
공식 코드LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems
https://github.com/ai4ce/TF-VPR
공식 코드https://github.com/ai4co/trajevo
공식 코드TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution
https://github.com/ai4he/toloka-cultural-assistant
공식 코드A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work Styles
https://github.com/ai4netzero/infinite_texture_gans
공식 코드Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture Synthesis
https://github.com/ai4pdlab/dpo_plm
공식 코드Guiding Generative Protein Language Models with Reinforcement Learning
https://github.com/aidanbx/coralai
공식 코드Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata Ecosystems
https://github.com/aidaramezani/cultural_inference
공식 코드https://github.com/aidos-lab/magnipy
공식 코드Metric Space Magnitude for Evaluating the Diversity of Latent Representations
https://github.com/aidos-lab/magnitude-diversity
공식 코드Metric Space Magnitude for Evaluating the Diversity of Latent Representations
https://github.com/aidotse/stylegan2-ada-pytorch
공식 코드GAN-based generative modelling for dermatological applications -- comparative study
https://github.com/ailab-cvc/videogen-eval
공식 코드VideoGen-Eval: Agent-based System for Video Generation Evaluation
https://github.com/aim-uofa/DiverGen
공식 코드https://github.com/aim3-ruc/rucm3ed
공식 코드M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database
https://github.com/aimagelab/mad
공식 코드Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas
https://github.com/aimagelab/show-control-and-tell
공식 코드Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions
https://github.com/aimansnigdha/ambiguous-medical-image-segmentation-using-diffusion-models
공식 코드https://github.com/aioz-ai/GCD
공식 코드https://github.com/air4children/hri2022
공식 코드Piloting Diversity and Inclusion Workshops in Artificial Intelligence and Robotics for Children
https://github.com/airi-institute/al_ats
공식 코드https://github.com/aitor-martinez-seras/snn-automotive-object-detection
공식 코드https://github.com/aitorormazabal/paraphrasing-from-parallel
공식 코드https://github.com/aitorzan3/awesome-self-supervised-time-series-anomaly-detection
공식 코드https://github.com/ajiljalal/code-cs-fairness
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.