https://github.com/JdeRobot/NeuralBehaviors
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
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/JiauZhang/hyperdreambooth
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
https://github.com/JimmyDoan1309/BEGAN---Tensorflow-implementation
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KaiyangZhou/pytorch-vsumm-reinforce
https://github.com/KandariSrinivas/Adversarial-Feature-Hallucination-Networks-for-Few-Shot-Learning
Adversarial Feature Hallucination Networks for Few-Shot Learning
https://github.com/KarhouTam/FL-bench
https://github.com/KbeautyHair/BaselineModel
https://github.com/Kelechukwu1/PidginUNMT
PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KohakuBlueleaf/HyperKohaku
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
https://github.com/Koukyosyumei/AIJack
https://github.com/KristofPusztai/MixReg
Improving Generalization in Reinforcement Learning with Mixture Regularization
https://github.com/Kurumi233/Mutual-Channel-Loss
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
https://github.com/Kyfafyd/MirrorGAN
MirrorGAN: Learning Text-to-image Generation by Redescription
https://github.com/Kyzarok/MScProject_AURORA_with_RNN
Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
https://github.com/L-Lighter/LightNet
Graph-guided Architecture Search for Real-time Semantic Segmentation
https://github.com/LIA-DiTella/VesselVAE
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
https://github.com/LUSSeg/ImageNetSegModel
https://github.com/LUSSeg/PASS
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Li-Chongyi/Ucolor
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/LiYuhangUSTC/Sketch2Face
https://github.com/LiangqunLu/DLForChatbot
https://github.com/LichenXia198/Deeping-Learning-project
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
https://github.com/LinearPi/OCR_Chinese
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/LouisRouss/Diffusion-Based-Model-for-Colorization
https://github.com/LubdaMax/Data-Science-1
https://github.com/Luvata/sliderspace-flux
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
https://github.com/ML-GSAI/EGSDE
https://github.com/MS-P3/code3/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MS-P3/code6/tree/main/parallel
Parallel Structures in Pre-training Data Yield In-Context Learning
https://github.com/MadPr0grammer/CF_Project
https://github.com/ManuMahadevaswamy/PEGASUS
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.