https://github.com/AnonSubm2021/TransStyleGAN
공식 코드Transforming the Latent Space of StyleGAN for Real Face Editing
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/AntoineVendeville/HowOpinionsCrystallise
공식 코드Towards control of opinion diversity by introducing zealots into a polarised social group
https://github.com/Anup-Deshmukh/TREC_background_linking
공식 코드IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles
https://github.com/Arker123/Divergent-Ensemble-Networks
공식 코드https://github.com/Aso-UniMelb/neural-fieldwork-guide
공식 코드Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection
https://github.com/AtmaHou/Seq2SeqDataAugmentationForLU
공식 코드Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
https://github.com/AtmaHou/UserSimulator
공식 코드A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
https://github.com/Att100/CP2M
공식 코드CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation
https://github.com/Augus1999/bayesian-flow-network-for-chemistry
공식 코드https://github.com/AutoML-4Paradigm/AutoSTR
공식 코드AutoSTR: Efficient Backbone Search for Scene Text Recognition
https://github.com/AvataGarde/In_Context_Diversification
공식 코드Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
https://github.com/AvestimehrResearchGroup/GPT-FL
공식 코드GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
https://github.com/BFH-AMI/BIAS
공식 코드https://github.com/BUGPLEASEOUT/LasHeR
공식 코드LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking
https://github.com/BWLONG/BeyondAttentiveTokens
공식 코드https://github.com/Bai-YT/ConsistencyTTA
공식 코드ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
https://github.com/Baojin-Huang/Webface-OCC
공식 코드https://github.com/BarqueroGerman/BeLFusion
공식 코드BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction
https://github.com/BigBinnie/GDPO
공식 코드No Preference Left Behind: Group Distributional Preference Optimization
https://github.com/Binyr/ASDA
공식 코드Adversarial Semantic Data Augmentation for Human Pose Estimation
https://github.com/BohanLi0110/NLP-DA-Papers
공식 코드Data Augmentation Approaches in Natural Language Processing: A Survey
https://github.com/BorealisAI/MMoEEx-MTL
공식 코드https://github.com/BorealisAI/keyphrase-generation
공식 코드Diverse Keyphrase Generation with Neural Unlikelihood Training
https://github.com/CGCL-codes/naturalcc
공식 코드CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code
https://github.com/CHENGY12/DisDis
공식 코드Personalized Trajectory Prediction via Distribution Discrimination
https://github.com/CIRS-Girona/ecarla-scenes
공식 코드eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction
https://github.com/CIRS-Girona/ewiz
공식 코드eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction
https://github.com/CLArg-group/argumentative-llms
공식 코드Argumentative Large Language Models for Explainable and Contestable Claim Verification
https://github.com/CRIPAC-DIG/TAGNN
공식 코드TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
https://github.com/CUHK-MMLAB/polynet
공식 코드PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
https://github.com/Canjie-Luo/Text-Image-Augmentation
공식 코드Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
https://github.com/CarstenEpic/humos
공식 코드https://github.com/Chakazul/Lenia
공식 코드https://github.com/ChangWeiTan/MultiRocket
공식 코드https://github.com/CharlieDinh/pFedMe
공식 코드https://github.com/Chen-Wang-CUHK/ExHiRD-DKG
공식 코드Exclusive Hierarchical Decoding for Deep Keyphrase Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.