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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/wangkua1/vmi
공식 코드https://github.com/wangqiang9/DiffSketching
공식 코드DiffSketching: Sketch Control Image Synthesis with Diffusion Models
https://github.com/wangren09/RAILS
공식 코드https://github.com/wangxb96/eode
공식 코드Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
https://github.com/wangyi111/cvppa2023-dnd-challenge
공식 코드Boosting High Resolution Image Classification with Scaling-up Transformers
https://github.com/wangzhihua520/SemiNCL_IQA
공식 코드Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
https://github.com/wangzy22/unipre3d
공식 코드UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting
https://github.com/warai-0toko/divaug
공식 코드DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
https://github.com/warmongeringbeaver/phendiff
공식 코드PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images
https://github.com/wchen459/PaDGAN
공식 코드PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs
https://github.com/webis-de/archive-query-log
공식 코드https://github.com/webis-de/sigir-23
공식 코드https://github.com/wei-mao-2019/gsps
공식 코드Generating Smooth Pose Sequences for Diverse Human Motion Prediction
https://github.com/weifantt/AutoFS
공식 코드AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
https://github.com/weijiawu/cisdq
공식 코드Continual Learning for Image Segmentation with Dynamic Query
https://github.com/weilabmsu/mdg
공식 코드https://github.com/weilunwang/semantic-diffusion-model
공식 코드https://github.com/weilunwang/sindiffusion
공식 코드SinDiffusion: Learning a Diffusion Model from a Single Natural Image
https://github.com/wenchen-chen/s4l-fsc
공식 코드Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification
https://github.com/wengong-jin/multiobj-rationale
공식 코드Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/wenhua-li/commea
공식 코드Coevolutionary Framework for Generalized Multimodal Multi-objective Optimization
https://github.com/wenhua-li/comparativestudyofmmop
공식 코드Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-Art
https://github.com/wenhuang2000/vhtest
공식 코드https://github.com/wenjiewwj/ucrs
공식 코드https://github.com/wenkaixl/steingen_code
공식 코드https://github.com/wenlai-lavine/sneuron-tst
공식 코드Style-Specific Neurons for Steering LLMs in Text Style Transfer
https://github.com/whytin/MVCVAE
공식 코드Shared Generative Latent Representation Learning for Multi-view Clustering
https://github.com/wielandbrendel/dPCA
공식 코드https://github.com/willard-yuan/hashing-baseline-for-image-retrieval
공식 코드https://github.com/willbradshaw/geac
공식 코드Analysis of the first Genetic Engineering Attribution Challenge
https://github.com/williamSYSU/CatGAN
공식 코드https://github.com/williamSYSU/TextGAN-PyTorch
공식 코드https://github.com/williamium3000/diverse-cotraining
공식 코드https://github.com/wish44165/uavdb
공식 코드UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection
https://github.com/withchencheng/ecml_pkdd_23_real
공식 코드REAL: A Representative Error-Driven Approach for Active Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.