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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/yamamura-k/ACG

공식 코드

Diversified Adversarial Attacks based on Conjugate Gradient Method

https://github.com/yamamura-k/reacg

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Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks

https://github.com/yanasgh/rafs

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Toward Robust Uncertainty Estimation with Random Activation Functions

https://github.com/yanasgh/rafs_code

공식 코드

Toward Robust Uncertainty Estimation with Random Activation Functions

https://github.com/yangbang18/clfm

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Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning

https://github.com/yangchen1997/wcdt

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WcDT: World-centric Diffusion Transformer for Traffic Scene Generation

https://github.com/yangliangwei/dgrec

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DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

https://github.com/yangliuy/HybridNCM

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A Hybrid Retrieval-Generation Neural Conversation Model

https://github.com/yangmeng96/mmvmvae-hippocampal

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Unity by Diversity: Improved Representation Learning in Multimodal VAEs

https://github.com/yanivbl6/BeyondSigProp

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Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

https://github.com/yanivbl6/fixup

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Is Feature Diversity Necessary in Neural Network Initialization?

https://github.com/yanivnik/sinfusion-code

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SinFusion: Training Diffusion Models on a Single Image or Video

https://github.com/yanxwb/griffin

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Griffin: Towards a Graph-Centric Relational Database Foundation Model

https://github.com/yanzhuguo/linguistic-diversity

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The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text

https://github.com/yanzhuguo/llm-diversity

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Benchmarking Linguistic Diversity of Large Language Models

https://github.com/yaojieliu/ECCV20-STDN

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On Disentangling Spoof Trace for Generic Face Anti-Spoofing

https://github.com/yaolu-zjut/ddinterpreter

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Can pre-trained models assist in dataset distillation?

https://github.com/yaolu-zjut/kcore

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RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets

https://github.com/yaoyaozhu19/bsda

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BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification

https://github.com/yaphabates/rocket

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Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models

https://github.com/yaringal/CLGP

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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

https://github.com/yashpatel5400/galgen

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Diffusion Models for Probabilistic Deconvolution of Galaxy Images

https://github.com/yaxingwang/SDIT

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SDIT: Scalable and Diverse Cross-domain Image Translation

https://github.com/yazhouzhu19/rpt

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Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

https://github.com/yazidjanati/cfgig

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Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance

https://github.com/yc14600/discriminative-representation-loss

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Semi-Discriminative Representation Loss for Online Continual Learning

https://github.com/yellowshippo/fluxgnn-icml2024

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Graph Neural PDE Solvers with Conservation and Similarity-Equivariance

https://github.com/yeon-lab/subgroupte

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SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification

https://github.com/yeongseoj/conkd

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Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation

https://github.com/yerbapage/detectcodegpt

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Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers

https://github.com/yeyimilk/llmgpt4o

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Problematic Tokens: Tokenizer Bias in Large Language Models

https://github.com/yhlleo/GMM-UNIT

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GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling

https://github.com/yhwang1990/code-fdm

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Streaming Algorithms for Diversity Maximization with Fairness Constraints

https://github.com/yhygao/Efficient_Data_Augmentation

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Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

https://github.com/yhzhai/atom

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Language-guided Human Motion Synthesis with Atomic Actions

https://github.com/yichen928/activeft

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Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm

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