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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/tianweiy/CenterPoint
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/tianyi-lab/ruler
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning
https://github.com/tiger-tiger/MOSES-2020
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/timbmg/DIAL-LV
https://github.com/timsainb/GAIA
https://github.com/tingchenfu/poisonbench
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
https://github.com/tom-tym/apg
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/tongbao96/code-for-sfr-as
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/topdu/openocr
AutoSTR: Efficient Backbone Search for Scene Text Recognition
https://github.com/touristCheng/Learning2Regrasp
https://github.com/tudarmstadt-lt/kaldi-tuda-de
https://github.com/twak/streetz
https://github.com/ubc-nlp/udistilwhisper
To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation
https://github.com/ubicomplab/rppg-toolbox
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
https://github.com/ugrulas/DivNoising
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/universome/alis
Aligning Latent and Image Spaces to Connect the Unconnectable
https://github.com/universome/stylegan-v
Towards Accurate Generative Models of Video: A New Metric & Challenges
https://github.com/urchade/molgen
https://github.com/utanaka2000/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/utkarshojha/few-shot-gan-adaptation
https://github.com/uyxela/Skin-Lesion-Classifier
https://github.com/val-iisc/deligan
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
https://github.com/vbnmzxc9513/GAN_BEGAN_human-face
https://github.com/vbnmzxc9513/GAN_BEGAN_hw2
https://github.com/vbookshelf/Skin-Lesion-Analyzer
https://github.com/vcheplygina/cats-scans
Cats or CAT scans: transfer learning from natural or medical image source datasets?
https://github.com/vectara/FaithJudge
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
https://github.com/ventr1c/res-gcl
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vid-koci/weightingGAP
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
https://github.com/videogorillas/colorize
https://github.com/vincentbillaut/all-colors-matter
https://github.com/vipin14119/Recommender_System_AutoEncoder
https://github.com/vita-group/abd-net
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