https://github.com/lmsdss/metamodulation
공식 코드MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
Papers with Code 아카이브
Diversity
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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/lnairgt/feature-aligned-diffusion
공식 코드Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
https://github.com/lojzezust/mmsegmentation-macvi
공식 코드LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
https://github.com/longfeiyun17/diversitycollapse
공식 코드https://github.com/lpeska/HT2020
공식 코드Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
https://github.com/lpeska/REVEAL2018
공식 코드Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
https://github.com/lporcaro/longterm-musdiv
공식 코드Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study
https://github.com/lporcaro/music-diversity-analysis
공식 코드https://github.com/lqtrung1998/mwp_cot_design
공식 코드https://github.com/ls4gan/benchmarking
공식 코드UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
https://github.com/ls4gan/uvcgan
공식 코드UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
https://github.com/lsy641/expectation-adjusted-distinct
공식 코드https://github.com/lt-nlp-lab-unibo/ethical-survey-speech
공식 코드https://github.com/ltronchin/gan-ensembles
공식 코드Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
https://github.com/ltronchin/latentaugment
공식 코드LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
https://github.com/luciusssss/mc2_corpus
공식 코드MC$^2$: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
https://github.com/luciusssss/zhuangbench
공식 코드Teaching Large Language Models an Unseen Language on the Fly
https://github.com/lugroupumn/icl-ellipticpdes
공식 코드In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
https://github.com/lugroupumn/icl_linear_systems
공식 코드In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
https://github.com/luka-group/odda-data-augmentation
공식 코드On-the-fly Denoising for Data Augmentation in Natural Language Understanding
https://github.com/lumi9587/learning-guided-SEM
공식 코드https://github.com/lunjohnzhang/warehouse_env_gen_public
공식 코드Multi-Robot Coordination and Layout Design for Automated Warehousing
https://github.com/luogen1996/Real-time-Global-Inference-Network
공식 코드A Real-time Global Inference Network for One-stage Referring Expression Comprehension
https://github.com/luowaterbi/ctf-instruct
공식 코드https://github.com/luowaterbi/multipot
공식 코드Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts
https://github.com/luoyan407/congruency
공식 코드Direction Concentration Learning: Enhancing Congruency in Machine Learning
https://github.com/lusinlu/biasloss_skipblocknet
공식 코드https://github.com/luxinyu1/sofa
공식 코드SoFA: Shielded On-the-fly Alignment via Priority Rule Following
https://github.com/luy0222/sirst-5k
공식 코드https://github.com/lxxxxr/kaleidoscope
공식 코드Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution
공식 코드Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
https://github.com/lyan62/FoodieQA
공식 코드FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
https://github.com/lychan110/remixing-graded-structures-data
공식 코드https://github.com/lygjwy/dos
공식 코드DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
https://github.com/lyingCS/UOEP
공식 코드https://github.com/lyleregenwetter/ndgms
공식 코드Constraining Generative Models for Engineering Design with Negative Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.