Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 21
- 보존된 평가 기록
- 570
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gouthamvgk/coreml_conversion_hub
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gpleiss/limits_of_large_width
https://github.com/gpspelle/Crowd-Counting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/greatsharma/DeepLearning-Papers-Implementation
https://github.com/griffinbran/machines_best_friend
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/guanqun-yang/reproducibility_challenge_neurips_2019
Learning Robust Global Representations by Penalizing Local Predictive Power
https://github.com/guilherme-pombo/keras_resnext_fpn
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/guilk/vlc
https://github.com/gundeep15/SCD_AI_Project
https://github.com/gupta-abhay/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gurkirt/realtime-action-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gusdnd852/ResNeXt-Parallel-Keras
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/haaz77/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/haiyang-w/git
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hanfried/hanfried-bookmarks
https://github.com/haomo-ai/superfusion
https://github.com/haonanguo/remote-sensing-chatgpt
https://github.com/harisankar95/AdvStyle
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
https://github.com/hasanjawad001/cglearn
https://github.com/hcygeorge/aoi_defect_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/healthx-lab/biomedcoop
https://github.com/henriquemeloo/cat-dog-image-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hershd23/ObjectLocalizer
https://github.com/hh-xiaohu/Image-augementation-pytorch
https://github.com/hhenryd/tap
https://github.com/hieu2906090/deep-learning-in-js
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hitchingsh/ml-examples
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/hiyouga/easyr1
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/hkbu-vscomputing/2022_mm_dmae-mocap
https://github.com/hmichaeli/alias_free_convnets
https://github.com/holdfire/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/holdfire/FAS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.