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/kingcong/UColor-MindSpore-
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/kingcong/augvit
https://github.com/kingsaint/Wikidata-Descriptions
https://github.com/kjsman/stable-diffusion-pytorch
https://github.com/kkang831/BDInvert_Release
https://github.com/kl-lab/fuma
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/kl-lab/gratis
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/knights207210/Deep-Learning-for-VUI
https://github.com/komiya-m/MirrorGAN
MirrorGAN: Learning Text-to-image Generation by Redescription
https://github.com/komyeongjin/specdiff-gan
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
https://github.com/kornia/kornia
https://github.com/koshian2/affinity-loss
https://github.com/kousw/experimental-consistory
https://github.com/kozistr/gan-metrics
Reliable Fidelity and Diversity Metrics for Generative Models
https://github.com/kpriyadarshini/BatchAML_Decorrelation
https://github.com/kunheek/style-aware-discriminator
https://github.com/kushalpatil1997/text_to_image_synthesis
https://github.com/kylelo/roofdiffusion
https://github.com/labbeti/aac-datasets
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/laion-ai/conditioned-prior
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/lastmile-ai/aiconfig
Automatic Chain of Thought Prompting in Large Language Models
https://github.com/layer6ai-labs/dgm_manifold_survey
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/lazaratan/gflownet-generalization
https://github.com/leaplabthu/uni-adafocus
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
https://github.com/lennelov/endd-reproduce
https://github.com/leofanzeres/s2i
Sound-to-imagination: Unsupervised cross- modal translation using deep dense network architecture.
https://github.com/leoliao2008/TensorflowMobileOfficialExample
https://github.com/leon-schi/ColorNet
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/liangxiaoxuan/Image-processing-DL
Improving Adversarial Robustness via Promoting Ensemble Diversity
https://github.com/libeineu/GPKD
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/libeineu/SDT-Training
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/lich14/CDS
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.