https://github.com/linkon87/exploring-diversity-based-active-learning-for-3d-object-detection-in-autonomous-driving
공식 코드Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
Papers with Code 아카이브
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/lins-lab/rded
공식 코드On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
https://github.com/linzwcs/pasted
공식 코드Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
https://github.com/lipang/hsigene
공식 코드HSIGene: A Foundation Model For Hyperspectral Image Generation
https://github.com/lipiji/Guyu
공식 코드https://github.com/lipzh5/x2cnet
공식 코드X2C: A Dataset Featuring Nuanced Facial Expressions for Realistic Humanoid Imitation
https://github.com/lirt1231/kermit
공식 코드KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
https://github.com/liruihui/PointAugment
공식 코드PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
https://github.com/liruiw/gensim
공식 코드GenSim: Generating Robotic Simulation Tasks via Large Language Models
https://github.com/lis-epfl/Tensoft-G21
공식 코드https://github.com/lisacoiffard/aurora-xcon
공식 코드Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity
https://github.com/lisadunlap/alia
공식 코드Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
https://github.com/littlehacker26/discriminator-cooperative-unlikelihood-prompt-tuning
공식 코드DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation
https://github.com/littleyaang/hq-50k
공식 코드HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
https://github.com/liubinggunzu/vlad-vsa
공식 코드VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptation
https://github.com/liuchenzhengyi/dimongen
공식 코드DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
https://github.com/liujf1226/mono-vifi
공식 코드https://github.com/liunian-harold-li/scotd
공식 코드Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
https://github.com/liushunyu/cia
공식 코드Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
https://github.com/liushunyu/opt
공식 코드Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
https://github.com/liuweide01/Cross-Image-Region-Mining-with-Region-Prototypical-Network-for-Weakly-Supervised-Segmentation
공식 코드Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation
https://github.com/liuxin0824/comsd
공식 코드ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
https://github.com/liuxubo717/sound_generation
공식 코드Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning
https://github.com/lizheren/MSVCL_MICCAI2021
공식 코드Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
https://github.com/lizhou21/cultural-compass
공식 코드https://github.com/lizlizli/destein
공식 코드https://github.com/ljcleo/agent_sense
공식 코드AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios
https://github.com/ljl0222/CUNIT
공식 코드https://github.com/ljyflores/simplification-project
공식 코드https://github.com/ll0ruc/divsr
공식 코드Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation
https://github.com/lliai/d2moe
공식 코드https://github.com/llm360/megamath
공식 코드https://github.com/llyx97/tempcompass
공식 코드https://github.com/lm-sys/fastchat
공식 코드LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/lmb-freiburg/diffusion-for-ood
공식 코드Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
https://github.com/lmeraser/lmeraser
공식 코드LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.