https://github.com/xiaosen-wang/sit
공식 코드Structure Invariant Transformation for better Adversarial Transferability
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/xiaoyinggit/dcrs
공식 코드https://github.com/xiefeng69/mixtea
공식 코드MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
https://github.com/ximinng/SVGDreamer
공식 코드https://github.com/xingchenwan/cell-based-nas-analysis
공식 코드On Redundancy and Diversity in Cell-based Neural Architecture Search
https://github.com/xingpingdong/pl-cfe
공식 코드Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
https://github.com/xinjinghao/color
공식 코드https://github.com/xinli-zn/informative-tracking-benchmark
공식 코드https://github.com/xinxingwu-uk/fae
공식 코드https://github.com/xinykou/FGDILP
공식 코드Fine-Grained Detoxification via Instance-Level Prefixes for Large Language Models
https://github.com/xinyu1205/idea-pytorch
공식 코드IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
https://github.com/xiongsiheng/tg-llm
공식 코드https://github.com/xiongyiheng/pt43d
공식 코드PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB Images
https://github.com/xiutian/GEDI
공식 코드An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making
https://github.com/xiwenc1/dra-grpo
공식 코드https://github.com/xkianteb/ApproPO
공식 코드https://github.com/xl-sr/THOR
공식 코드https://github.com/xmed-lab/triplee-dg
공식 코드https://github.com/xmichelleshihx/AL_Skin_SAAS
공식 코드An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
https://github.com/xmu-xiaoma666/DTNet
공식 코드https://github.com/xuchen0427/fairdiverse
공식 코드FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval Algorithms
https://github.com/xushizhou/whomp
공식 코드WHOMP: Optimizing Randomized Controlled Trials via Wasserstein Homogeneity
https://github.com/xuxw98/backtoreality
공식 코드Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
https://github.com/xuyangabert/alcs
공식 코드https://github.com/xwhan/DeepPath
공식 코드DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/xydaytoy/BMI-NMT
공식 코드Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
https://github.com/xypan0/G-DIG
공식 코드https://github.com/xzhang00/qstr
공식 코드A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
https://github.com/xzhang97666/alpacare
공식 코드AlpaCare:Instruction-tuned Large Language Models for Medical Application
https://github.com/xzwlx/Difficulty-SR
공식 코드Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
https://github.com/y-zheng18/point_odyssey
공식 코드PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
https://github.com/y0mingzhang/diffuse-distributions
공식 코드https://github.com/y0mingzhang/diffuse-probabilities
공식 코드https://github.com/yafuly/syntacticgen
공식 코드https://github.com/yahiaetman/ms-level-gen
공식 코드https://github.com/yale-lily/loft
공식 코드LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.