Papers with Code 아카이브

Diversity

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/jimmy-dq/dropmae

공식 코드

DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks

https://github.com/jinaleejnl/alignx

공식 코드

From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

https://github.com/jindongwang/transferlearning

공식 코드

Domain-invariant Feature Exploration for Domain Generalization

https://github.com/jinghangli98/wmh_seg

공식 코드

wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T

https://github.com/jingtao-li-cver/uniadrs

공식 코드

Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery

https://github.com/jingweio/LGD-GCN

공식 코드

LGD-GCN: Local and Global Disentangled Graph Convolutional Networks

https://github.com/jinlab-imvr/resurgsam2

공식 코드

ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking

https://github.com/jinnh/resss-convset

공식 코드

Deep Diversity-Enhanced Feature Representation of Hyperspectral Images

https://github.com/jitinkrishnan/Diversity-Based-Generalization

공식 코드

Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift

https://github.com/jiwanhur/unlockmgm

공식 코드

Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance

https://github.com/jiweiqi/CellBox.jl

공식 코드

Inference of cell dynamics on perturbation data using adjoint sensitivity

https://github.com/jiwoogit/dcp-gan

공식 코드

Diversity-aware Channel Pruning for StyleGAN Compression

https://github.com/jjessicayao/aist-m-dataset

공식 코드

Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models

https://github.com/jkarolczak/important-parts-of-prototypes

공식 코드

This part looks alike this: identifying important parts of explained instances and prototypes

https://github.com/jmtomczak/reversible-de

공식 코드

Differential Evolution with Reversible Linear Transformations

https://github.com/jniemeijer/didex

공식 코드

Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation

https://github.com/joanagoncalveslab/DCAST

공식 코드

DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

https://github.com/joanagoncalveslab/metric-dst

공식 코드

Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning

https://github.com/joeyz0z/conzic

공식 코드

ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

https://github.com/johndmendonca/maia-dqe

공식 코드

Dialogue Quality and Emotion Annotations for Customer Support Conversations

https://github.com/johnnychanv/diffusioncls

공식 코드

An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification

https://github.com/jokieleung/I-MCTS

공식 코드

I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search

https://github.com/jonas-becker/pd-human-vs-machine-content

공식 코드

Paraphrase Detection: Human vs. Machine Content

https://github.com/jordan-hs/diversity_is_definitely_needed

공식 코드

Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion

https://github.com/jorge-martinez-gil/oruga

공식 코드

Optimizing Readability Using Genetic Algorithms

https://github.com/joseeliel/lightweight_genetic_algorithm

공식 코드

A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces

https://github.com/joshinh/investigation-cad

공식 코드

An Investigation of the (In)effectiveness of Counterfactually Augmented Data

https://github.com/joshr17/slc_sampling

공식 코드

Flexible Modeling of Diversity with Strongly Log-Concave Distributions

https://github.com/jparkerholder/DvD_ES

공식 코드

Effective Diversity in Population Based Reinforcement Learning

https://github.com/jpbruneton/QDSR

공식 코드

Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired Constraints

https://github.com/jpwahle/emnlp23-citation-field-influence

공식 코드

We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields

https://github.com/jpwahle/emnlp24-prompt-paraphrase

공식 코드

Paraphrase Types Elicit Prompt Engineering Capabilities

https://github.com/jpwahle/prompt-paraphrase

공식 코드

Paraphrase Types Elicit Prompt Engineering Capabilities

https://github.com/jspenmar/slowtv_monodepth

공식 코드

Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV

https://github.com/jsun57/comusion

공식 코드

CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion

https://github.com/jtonglet/seer

공식 코드

SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA

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