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/viiika/diffusion-conductor

공식 코드

Taming Diffusion Models for Music-driven Conducting Motion Generation

https://github.com/vijaykeswani/Diversity-Audit-Using-Representative-Examples

공식 코드

Auditing for Diversity using Representative Examples

https://github.com/vila-lab/delt

공식 코드

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation

https://github.com/vimar-gu/minimaxdiffusion

공식 코드

Efficient Dataset Distillation via Minimax Diffusion

https://github.com/vinairesearch/swiftbrushv2

공식 코드

SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teacher

https://github.com/vincent-leguen/DILATE

공식 코드

Deep Time Series Forecasting with Shape and Temporal Criteria

https://github.com/vincent-leguen/STRIPE

공식 코드

Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

https://github.com/vincentdengp/3d-lr

공식 코드

Can 3D Vision-Language Models Truly Understand Natural Language?

https://github.com/violet-sto/hn-gfn

공식 코드

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

https://github.com/vipulgupta1011/calm

공식 코드

CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias

https://github.com/virajprabhu/clue

공식 코드

Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings

https://github.com/vis-var/lgsc-for-fas

공식 코드

Learning Generalized Spoof Cues for Face Anti-spoofing

https://github.com/vishakhpk/hai-diversity

공식 코드

Does Writing with Language Models Reduce Content Diversity?

https://github.com/visinf/cos-cvae

공식 코드

Diverse Image Captioning with Context-Object Split Latent Spaces

https://github.com/visinf/style-seqcvae

공식 코드

Diverse Image Captioning with Grounded Style

https://github.com/visipedia/herbarium_comp

공식 코드

The Herbarium 2021 Half-Earth Challenge Dataset

https://github.com/visresearch/DGMR

공식 코드

Diversity-Guided MLP Reduction for Efficient Large Vision Transformers

https://github.com/vita-epfl/stay-on-track

공식 코드

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses

https://github.com/vita-epfl/unitraj

공식 코드

UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction

https://github.com/vita-group/augmax

공식 코드

AugMax: Adversarial Composition of Random Augmentations for Robust Training

https://github.com/vita-group/diverse-vit

공식 코드

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

https://github.com/vita-group/mak

공식 코드

Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling

https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries

공식 코드

A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries

https://github.com/vitiugin/elmict

공식 코드

Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on Reddit

https://github.com/vivoutlaw/SSIAM

공식 코드

Self-Supervised Learning of Face Representations for Video Face Clustering

https://github.com/vkalogeiton/yto-dataset

공식 코드

Analysing domain shift factors between videos and images for object detection

https://github.com/vojha-code/Neural-Tree-Software

공식 코드

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

https://github.com/voldemortX/DST-CBC

공식 코드

UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation

https://github.com/vturrisi/disef

공식 코드

Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

https://github.com/vvictoryuki/cross

공식 코드

CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography

https://github.com/wadeyin9712/geomlama

공식 코드

GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models

https://github.com/walegahaha/Curls-Whey

공식 코드

Curls & Whey: Boosting Black-Box Adversarial Attacks

https://github.com/walkermitty/emoava

공식 코드

Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation Benchmark

https://github.com/walsharry/Sign_Stitching_Demos

공식 코드

Sign Stitching: A Novel Approach to Sign Language Production

https://github.com/wang-chaoyang/semflow

공식 코드

SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow

https://github.com/wangcunxiang/graph-as-tokens

공식 코드

Exploiting Abstract Meaning Representation for Open-Domain Question Answering

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