https://github.com/jamaltoutouh/eel-gans
공식 코드Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
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/jamesqian11/subsa
공식 코드Sub-SA: Strengthen In-context Learning via Submodular Selective Annotation
https://github.com/janak11111/diverse-icl
공식 코드Exploring the Role of Diversity in Example Selection for In-Context Learning
https://github.com/janetlauyeung/crossGENRE4RST
공식 코드Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity
https://github.com/jaryP/StructuredEnsemble
공식 코드Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
https://github.com/jasonnikolaou/tf-conflicts
공식 코드https://github.com/jaysquare87/resilience_from_diversity
공식 코드Resilience from Diversity: Population-based approach to harden models against adversarial attacks
https://github.com/jc-ryan/holistic_automated_red_teaming
공식 코드https://github.com/jcyk/skeleton-to-response
공식 코드Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
https://github.com/jedota/Semi-Auto-Transfer-PS600
공식 코드Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications
https://github.com/jedota/Style-Transfer-PS600
공식 코드Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications
https://github.com/jedota/flickr-pad
공식 코드Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database
https://github.com/jeffwang987/openoccupancy
공식 코드OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
https://github.com/jegarcian/AI4Ecology
공식 코드A Deep Generative Artificial Intelligence system to decipher species coexistence patterns
https://github.com/jenna-fromer/qpo
공식 코드Batched Bayesian optimization by maximizing the probability of including the optimum
https://github.com/jeongwhanchoi/scone
공식 코드https://github.com/jeonjaehyeong/dpl
공식 코드Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
https://github.com/jeremy-costello/rl-for-topic-models
공식 코드https://github.com/jeremyxsc/finegpr
공식 코드Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
https://github.com/jerrrynie/diverseqa
공식 코드https://github.com/jfzhang95/PoseAug
공식 코드PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
https://github.com/jgalle29/gait
공식 코드https://github.com/jhcknzzm/SSFL-Benchmarking-Semi-supervised-Federated-Learning
공식 코드Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
https://github.com/jhu-clsp/kreyol-mt
공식 코드Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages
https://github.com/jiacheng-ye/kg_one2set
공식 코드https://github.com/jiachengli1995/ucepic
공식 코드https://github.com/jiaju-chen/dlcrec
공식 코드DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
https://github.com/jialuli-luka/envedit
공식 코드EnvEdit: Environment Editing for Vision-and-Language Navigation
https://github.com/jianlong-yuan/semi-mmseg
공식 코드Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
https://github.com/jianzhangcs/COAST
공식 코드COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing
https://github.com/jiaqisjtu/iterit
공식 코드Boosting LLM via Learning from Data Iteratively and Selectively
https://github.com/jiawei151/sgiel_vireid
공식 코드Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
https://github.com/jiaxianghuang/seqmo
공식 코드Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence Model
https://github.com/jiaxiaojunqaq/sa-aet
공식 코드Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack
https://github.com/jiayichen815/feal
공식 코드https://github.com/jiayinlp/insbank
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.