https://github.com/dongwoohhh/GS-Blur
공식 코드GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring
Papers with Code 아카이브
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/doubledaibo/drnet
공식 코드Detecting Visual Relationships with Deep Relational Networks
https://github.com/dqxiu/ParaSCI
공식 코드ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
https://github.com/dragnet-org/dragnet
공식 코드Predicting the Politics of an Image Using Webly Supervised Data
https://github.com/dragonlzm/ISAL
공식 코드https://github.com/dream-lm/ingb_framework
공식 코드INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification
https://github.com/dreamfold/foldflow
공식 코드Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
https://github.com/dreamtalecore/scs-uit
공식 코드Separating Content and Style for Unsupervised Image-to-Image Translation
https://github.com/drndr/gencodesearchnet
공식 코드https://github.com/droliven/diverse_sampling
공식 코드Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space
https://github.com/drwuHUST/iGS
공식 코드https://github.com/drwuHUST/sAL
공식 코드https://github.com/ds4sd/molgrapher
공식 코드MolGrapher: Graph-based Visual Recognition of Chemical Structures
https://github.com/dsshim0125/FS-NCSR
공식 코드https://github.com/dsysdml/fef
공식 코드Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics
https://github.com/dtak/lit
공식 코드https://github.com/dtch1997/rl_cbf
공식 코드Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory
https://github.com/dtuzi123/ovae
공식 코드Continual Variational Autoencoder Learning via Online Cooperative Memorization
https://github.com/duangzhu/maexp
공식 코드MAexp: A Generic Platform for RL-based Multi-Agent Exploration
https://github.com/dulyhao/alphaforge
공식 코드AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
https://github.com/dundalia/twolar
공식 코드TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking
https://github.com/dunnolab/xland-minigrid
공식 코드XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
https://github.com/dunzeng/fedaware
공식 코드On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
https://github.com/duongnhatthang/BOSS
공식 코드Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits
https://github.com/duxiaodan/fast_text2stylegan
공식 코드Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
https://github.com/duykhuongnguyen/recourse-plan-diverse-interpolation
공식 코드https://github.com/dvirsamuel/SeedSelect
공식 코드Generating images of rare concepts using pre-trained diffusion models
https://github.com/dvl-tum/ciagan
공식 코드CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
https://github.com/dvlab-research/magicmirror
공식 코드Magic Mirror: ID-Preserved Video Generation in Video Diffusion Transformers
https://github.com/dwlmt/knowledgeable-stories
공식 코드https://github.com/dx118/dynaip
공식 코드https://github.com/dx199771/interpretability-aqa
공식 코드https://github.com/dykang/PASTEL
공식 코드https://github.com/dynamicslab/modified-SINDy
공식 코드https://github.com/eXascaleInfolab/daoc
공식 코드https://github.com/eailer/causal-compositions
공식 코드Instrumental Variable Estimation for Compositional Treatments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.