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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/VamshiTeja/SMDL

공식 코드

Submodular Batch Selection for Training Deep Neural Networks

https://github.com/VectorInstitute/GenerativeSSL

공식 코드

Can Generative Models Improve Self-Supervised Representation Learning?

https://github.com/VinAIResearch/input-aware-backdoor-attack-release

공식 코드

Input-Aware Dynamic Backdoor Attack

https://github.com/Vincent-ZHQ/MSER

공식 코드

Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages

https://github.com/VirtualEmbryo/blastocoel_review

공식 코드

Blastocoel morphogenesis: a biophysics perspective

https://github.com/Vivian-wzj/contributing-dimension-structure

공식 코드

Contributing Dimension Structure of Deep Feature for Coreset Selection

https://github.com/WHUIR/PPVAE

공식 코드

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders

https://github.com/Wang-xd1899/SSHT

공식 코드

Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer

https://github.com/WanzhengZhu/GPS

공식 코드

Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech

https://github.com/Westlake-AGI-Lab/Distill-Any-Depth

공식 코드

Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator

https://github.com/WonderSeven/ULDA

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Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

https://github.com/XGenerationLab/XiYan-SQL

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A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/XiaokunSun/Barbie

공식 코드

Barbie: Text to Barbie-Style 3D Avatars

https://github.com/XiaokunSun/StrandHead

공식 코드

StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric Priors

https://github.com/XinnuoXu/CVAE_Dial

공식 코드

Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity

https://github.com/XinyiYing/RGBT-Tiny

공식 코드

Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines

https://github.com/Xtra-Computing/VertiBench

공식 코드

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

https://github.com/XuweiyiChen/UniCtrl

공식 코드

UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control

https://github.com/YDU-uva/VSM_WSD

공식 코드

Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

https://github.com/Yan98/S2FGAN

공식 코드

S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation

https://github.com/YangLIN1997/AMLNet-DSAA2023

공식 코드

AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting

https://github.com/YangYongJin/FiFA-Official-Repo

공식 코드

Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models

https://github.com/Yanyi-Zhang/CompILer

공식 코드

Not Just Object, But State: Compositional Incremental Learning without Forgetting

https://github.com/YecanLee/2BeOETG

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Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework

https://github.com/YecanLee/Adaptive-Contrastive-Search

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Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation

https://github.com/YingzhenLi/SteinGrad

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Gradient Estimators for Implicit Models

https://github.com/YipingNUS/slogan-generation-dataset

공식 코드

Toward Improving Coherence and Diversity of Slogan Generation

https://github.com/YoungTimmy/SAG

공식 코드

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

https://github.com/Yueeey/deepsketch

공식 코드

Deep Sketch-Based Modeling: Tips and Tricks

https://github.com/YuhangSong/DEHRL

공식 코드

Diversity-Driven Extensible Hierarchical Reinforcement Learning

https://github.com/Yuxin-CV/DTN

공식 코드

Diversity Transfer Network for Few-Shot Learning

https://github.com/ZPdesu/II2S

공식 코드

Improved StyleGAN Embedding: Where are the Good Latents?

https://github.com/Ze-Yang/Context-Transformer

공식 코드

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

https://github.com/ZhangXiao96/OptimizationVariance

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Optimization Variance: Exploring Generalization Properties of DNNs

https://github.com/ZhaoyangLyu/VerifiablyRobustNN

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Towards Evaluating and Training Verifiably Robust Neural Networks

https://github.com/ZhengZerong/DeepHuman

공식 코드

DeepHuman: 3D Human Reconstruction from a Single Image

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