https://github.com/lightblue-tech/multilingual-mt-bench
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
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/linhuixiao/hivg
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
https://github.com/linhuixiao/oneref
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
https://github.com/liuf1990/CFSM
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
https://github.com/liupei101/psemix
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification
https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
https://github.com/liuzhengzhe/dreamstone-iss
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
https://github.com/liyang619/cole-platform
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
https://github.com/liyinqi/un2clip
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/liyuantsao/BFSR
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/liyuantsao/flowsr-lp
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/lllyasviel/controlnet
MaskSketch: Unpaired Structure-guided Masked Image Generation
https://github.com/lllyasviel/fooocus
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
https://github.com/lmnt-com/diffwave
https://github.com/locuslab/gradient_regularized_gan
https://github.com/lojzezust/lars_evaluator
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
https://github.com/lonce/sonyGanFork
https://github.com/lorenzofamiglini/Irony-Sarcasm-Detection-Task
https://github.com/louaaron/scaling-riemannian-diffusion
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/lpeska/FUZZ-IEEE2020
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
https://github.com/lpeska/GMAP2025
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
https://github.com/lsh110600/person_re_id
https://github.com/lucashu1/land-cover
https://github.com/lucidrains/CoCa-pytorch
https://github.com/lucidrains/DALLE2-pytorch
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/lucidrains/FLASH-pytorch
https://github.com/lucidrains/classifier-free-guidance-pytorch
https://github.com/lucidrains/ddpm-proteins
https://github.com/lucidrains/mammut-pytorch
https://github.com/lucidrains/phenaki-pytorch
https://github.com/lucidrains/progen
https://github.com/lucidrains/progen-jax
https://github.com/lucidrains/voicebox-pytorch
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
https://github.com/lucidrains/x-clip
https://github.com/luciusssss/mlic-eval
MC$^2$: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.