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/vlomonaco/US-TransportationMode
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
https://github.com/volcengine/vegiantmodel
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vvvm23/vqvae-2
https://github.com/wanboyang/Anomaly_AR_Net_ICME_2020
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
https://github.com/wang-axis/dna
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
https://github.com/wangcongcong123/ttt
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/wangxiao5791509/DeepMTA_PyTorch
https://github.com/wangyongjie-ntu/CFAI
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
https://github.com/wangyongjie-ntu/Counterfactual-Explanations-Pytorch
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
https://github.com/wannabeOG/MAS-PyTorch
Gradient based sample selection for online continual learning
https://github.com/wanyu2018umac/Unsupervised_Dialect_Translation
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
https://github.com/webis-de/small-text
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
https://github.com/wenet-e2e/wespeaker
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
https://github.com/wenh06/fl-sim
https://github.com/whatissimondoing/his-dialog
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
https://github.com/wikidepia/indonesian_datasets
https://github.com/wilson1yan/VideoGPT
Towards Accurate Generative Models of Video: A New Metric & Challenges
https://github.com/windweller/PragmaticVQA
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/wish44165/ntd
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
https://github.com/witcher425/CHINESEOCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/witnessai/Awesome-Zero-Shot-Object-Detection
https://github.com/woctezuma/steam-CLIP
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/wqliu657/TransNetV2
TransNet V2: An effective deep network architecture for fast shot transition detection
https://github.com/wsntxxn/AudioCaption
https://github.com/wtjiang98/mmpose-gyl
https://github.com/wy1iu/OPT
https://github.com/wyhuai/SkillMimic
SkillMimic: Learning Basketball Interaction Skills from Demonstrations
https://github.com/xanaduai/iqpopt
Understanding Deep Generative Models with Generalized Empirical Likelihoods
https://github.com/xcfcode/Summarization-Papers
https://github.com/xgenerationlab/xiyan-dbdescgen
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/xgenerationlab/xiyan_mcp_server
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/xiandaguo/openstereo
https://github.com/xiaofengShi/CHINESE-OCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/ximinng/pytorch-svgrender
https://github.com/xingyuliuNLP/ResponseEdit_FR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.