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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/mlfoundations/open_clip
https://github.com/mlfoundations/tabliblib
https://github.com/mlvc-lab/BeGan_pytorch
https://github.com/mohamad-m2/federated-learning
Federated Visual Classification with Real-World Data Distribution
https://github.com/mohamedgabr96/NeuralDialectDetector
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
https://github.com/monoclecat/latent-conditioned-SAC
https://github.com/mop/bier
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
https://github.com/morgangiraud/leniax
https://github.com/mortezagolzan/Face-Anti-Spoofing
https://github.com/moseslichten/TAFSSL
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
https://github.com/mostafachatillon/ChemGAN-challenge
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
https://github.com/mousecpn/L2A-OT
Learning to Generate Novel Domains for Domain Generalization
https://github.com/mv-lab/kuzushiji-recognition
https://github.com/myuli/sert
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
https://github.com/myungkeun-cho/taobao_dlrm
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
https://github.com/n9839950/EGH400-2-DSN
https://github.com/nakhunchumpolsathien/TR-TPBS
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
https://github.com/nakhunchumpolsathien/ThaiSum
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
https://github.com/nathanlesage/so-classifier
https://github.com/naver-ai/StyleMapGAN
https://github.com/navneet-nmk/Hierarchical-Meta-Reinforcement-Learning
Diversity is All You Need: Learning Skills without a Reward Function
https://github.com/nchang430/BOLD5000-Scripts
https://github.com/ncoop57/gpt-code-clippy
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/neda60/video-summarization-using-FCSN
https://github.com/neeleshnegi/chat-bot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/neillu23/DiffuSE
https://github.com/neillu23/cdiffuse
https://github.com/nerdslab/myow
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
https://github.com/neurai-lab/dare
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
https://github.com/neutralzz/billa
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/newspapersegmentation/newspaperimagesegmentation
dhSegment: A generic deep-learning approach for document segmentation
https://github.com/nguyetn89/Anomaly_detection_ICCV2019
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
https://github.com/nikithamalgifb/dlrm
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
https://github.com/nirvanalan/ln3diff
3DGen: Triplane Latent Diffusion for Textured Mesh Generation
https://github.com/njucckevin/seeclick
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.