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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/yxuansu/contrastive_search_is_what_you_need

An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation

https://github.com/yxuansu/magic

A Contrastive Framework for Neural Text Generation

https://github.com/yzyouzhang/AIR-ASVspoof

SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing

https://github.com/zcemycl/Matlab-GAN

Conditional Image Synthesis With Auxiliary Classifier GANs

https://github.com/ze1gades/diary

SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation

https://github.com/zejun-yang/aniportrait

AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation

https://github.com/zfw1226/gym-unrealcv

Towards Active Vision for Action Localization with Reactive Control and Predictive Learning

https://github.com/zhangbo-nlp/TransIKG

Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

https://github.com/zhanghainan/RNN-S2S

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/RNN-encdec

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/ReinforcingCoherence

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/TailoredSeq2Seq2DifferentConversationScenarios

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/s2s-with-attention

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhengyuzhao/koniq-pytorch

KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment

https://github.com/zhiqic/chartreader

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/zhoubolei/moments_models

Moments in Time Dataset: one million videos for event understanding

https://github.com/zhusiling/BEGAN_org

BEGAN: Boundary Equilibrium Generative Adversarial Networks

https://github.com/zi-w/Ensemble-Bayesian-Optimization

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

https://github.com/zion-king/Deep-Learning-for-Person-Re-identification

ABD-Net: Attentive but Diverse Person Re-Identification

https://github.com/ziqi-ma/find3d

Find Any Part in 3D

https://github.com/zju-vipa/cmi

Contrastive Model Inversion for Data-Free Knowledge Distillation

https://github.com/zshyang/amg

Dyadic Interaction Modeling for Social Behavior Generation

https://github.com/zuocx1996/tic

Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors

https://github.com/zzmshinnosuke/sketch

Photo-Sketching: Inferring Contour Drawings from Images

https://github.com/zzz2010/starganv2_paddle

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://gitlab.com/viper-purdue/ocil-real-world-food-image-classification

Gradient based sample selection for online continual learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.