https://github.com/melosy/cam
공식 코드Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
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/melsunny/keras-fcn
공식 코드Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
https://github.com/memray/OpenNMT-kpg-release
공식 코드One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
https://github.com/mercari/mercari-ml-merrec-pub-us
공식 코드MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
https://github.com/mertan-a/towards-multi-morphology-controllers
공식 코드Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
https://github.com/metunlp/openethics
공식 코드OpenEthics: A Comprehensive Ethical Evaluation of Open-Source Generative Large Language Models
https://github.com/mgholamikn/AdaptPose
공식 코드AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation
https://github.com/mgreenig/loopgen
공식 코드Score-Based Generative Models for Designing Binding Peptide Backbones
https://github.com/mhealthbuet/mpox-skin-lesion-dataset-v2
공식 코드https://github.com/michael-beukman/pcgnn
공식 코드Towards Objective Metrics for Procedurally Generated Video Game Levels
https://github.com/michael-beukman/robocupgym
공식 코드RobocupGym: A challenging continuous control benchmark in Robocup
https://github.com/michael-s-yao/dynamo
공식 코드Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
https://github.com/michaelhodel/re-arc
공식 코드Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation
https://github.com/michuanhaohao/AICITY2021_Track2_DMT
공식 코드An Empirical Study of Vehicle Re-Identification on the AI City Challenge
https://github.com/microsoft/aura
공식 코드Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement
https://github.com/microsoft/cream
공식 코드MiniViT: Compressing Vision Transformers with Weight Multiplexing
https://github.com/microsoft/dcvc
공식 코드https://github.com/microsoft/frame-flow
공식 코드https://github.com/microsoft/lamar-benchmark
공식 코드LaMAR: Benchmarking Localization and Mapping for Augmented Reality
https://github.com/microsoft/lmops
공식 코드$Se^2$: Sequential Example Selection for In-Context Learning
https://github.com/microsoft/protein-frame-flow
공식 코드https://github.com/microsoft/robustlearn
공식 코드https://github.com/microsoft/swin3d
공식 코드Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene Understanding
https://github.com/microsoft/topic_conversation
공식 코드https://github.com/microsoft/unisumm
공식 코드UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
https://github.com/migs2021/migs
공식 코드https://github.com/mikehuisman/transfer-meta-feature-representations
공식 코드Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques
https://github.com/mikelasz/benchmarking-fairness-imageupsampling
공식 코드https://github.com/milliema/invreg
공식 코드https://github.com/miltonllera/meta-evolved-dev
공식 코드https://github.com/mindhunter01/rmoa
공식 코드RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation
https://github.com/mindspore-ai/mindscience/tree/master/MindSPONGE/applications/MEGAProtein
공식 코드PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
https://github.com/minghu0830/ophnet-benchmark
공식 코드OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
https://github.com/mingyuan-zhang/ReMoDiffuse
공식 코드https://github.com/mingyuanzhou/sid-lsg
공식 코드https://github.com/minimel/stochasticbatchal
공식 코드Active learning for medical image segmentation with stochastic batches
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.