https://github.com/physiologicailab/factorizephys
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
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/piekey1994/IOM
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/piyushverma001/Colorizer
https://github.com/plnguyen2908/UniTalk-ASD-code
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
https://github.com/pmcgrath249/DMMFODS
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pmcgrath249/DeepCVLab
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pmcgrath249/DeepCV_Lab
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pootiet/augcse
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
https://github.com/prajjwal1/adaptive_transformer
https://github.com/prajjwal1/fluence
https://github.com/prajwaldp/gan-image-colorizer
https://github.com/prakashpandey9/BicycleGAN
https://github.com/prakhar21/textaugmentation-gpt2
https://github.com/pranavbudhwant/ACVAEGAN
https://github.com/prathyusha995912/prathyu
https://github.com/prathyusha995912/prathyusha
https://github.com/pravesh-bawangade/Image-colorization-using-Autoencoders
https://github.com/pritishuplavikar/All-In-One-Underwater-Image-Enhancement-using-Domain-Adversarial-Learning
All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
https://github.com/ptillet/torch-blocksparse
https://github.com/pvl/wikihow_pairs_dataset
https://github.com/pwc-1/Paper-9/tree/main/2/musicgen
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation
https://github.com/pwc-1/Paper-9/tree/main/4/VAE-Creative-Discovery-using-QD-Search
https://github.com/pwc-1/Paper-9/tree/main/7/Profiling-Pareto-Front
Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/pwc-1/Paper-9/tree/main/7/SVGD
https://github.com/pwc-1/Paper-9/tree/main/7/VAE-Creative-Discovery-using-QD-Search
https://github.com/pwc-1/Paper-9/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/qheldiv/shapeformer
ShapeFormer: Transformer-based Shape Completion via Sparse Representation
https://github.com/qibinc/KOBE
https://github.com/qijindou/CRAB
Multi-Label Bayesian Active Learning with Inter-Label Relationships
https://github.com/qingzwang/DiverseImageCaptioning
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process
https://github.com/qingzwang/DiversityMetrics
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/radiabouhali/test
https://github.com/rakshithShetty/captionGAN
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
https://github.com/raphaelBarman/dhSegment
dhSegment: A generic deep-learning approach for document segmentation
https://github.com/rawmarshmellows/deep-photo-aesthetics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.