https://github.com/QipengGuo/CycleGT
공식 코드Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
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/RamnathKumar181/Task-Diversity-meta-learning
공식 코드https://github.com/Raul2718/FLUXSynID
공식 코드https://github.com/RayeRen/multilingual-kd-pytorch
공식 코드Multilingual Neural Machine Translation with Knowledge Distillation
https://github.com/RenMin1991/SIDE
공식 코드Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
https://github.com/ReneeYe/VariationalTemplateMachine
공식 코드https://github.com/RowitZou/RankAE
공식 코드Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders
https://github.com/Rowl1ng/Fine-Grained_VR_Sketching
공식 코드https://github.com/RozDavid/LanguageGroundedSemseg
공식 코드Language-Grounded Indoor 3D Semantic Segmentation in the Wild
https://github.com/Rqcker/SymbioticAIS
공식 코드SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm
https://github.com/RuishanLiu/GAN-TSC
공식 코드Teacher-Student Compression with Generative Adversarial Networks
https://github.com/SCL-UT/face-manifold
공식 코드Face Manifold: Manifold Learning for Synthetic Face Generation
https://github.com/SEACrowd/seacrowd-datahub
공식 코드SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
https://github.com/SEACrowd/seacrowd-experiments
공식 코드SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
https://github.com/SEU-ProactiveSecurity-Group/LLM-PD
공식 코드Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense
https://github.com/SHIXOOM/PAIR
공식 코드PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms
https://github.com/SJTUHaiyangYu/BackdoorMBTI
공식 코드BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
https://github.com/SRI-AIC/InterestingnessXRL
공식 코드https://github.com/SSAW14/Image_Generation_with_Latent_Code
공식 코드Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/SaharR1372/DM_Style_matching
공식 코드https://github.com/SalesforceAIResearch/xLAM
공식 코드ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
https://github.com/SamsungLabs/CIC-BART-SSA
공식 코드CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation
https://github.com/SamySam0/SynDeidMLM
공식 코드https://github.com/Santa-Clara-Media-Lab/SoloPose
공식 코드SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation
https://github.com/Sanyuan-Chen/C2C-DA
공식 코드C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
https://github.com/SaraJalali/FairEmployeeAssignment
공식 코드https://github.com/Sausage-SONG/Few-shot-action-recognition
공식 코드https://github.com/Serega6678/NuNER
공식 코드NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-of-Body-Motion-Data-by-Neural-Gas-Network-for-Emotion-Recognition
공식 코드Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition
https://github.com/ShaojieJiang/FACE
공식 코드Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
https://github.com/Shark-NLP/DiffuSeq
공식 코드DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
https://github.com/Shivanshu-Gupta/structural-diversity
공식 코드Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation
https://github.com/Shorya-Sharma/Game-Theory-For-Adversarial-Attacks-And-Defenses
공식 코드https://github.com/ShotaSHIBASAKI/Switching_Environment
공식 코드Exclusion of the fittest predicts microbial community diversity in fluctuating environments
https://github.com/ShuyangCao/open-ended_question_ontology
공식 코드Controllable Open-ended Question Generation with A New Question Type Ontology
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.