https://github.com/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hongkunsun/paratranscnn
https://github.com/hongyi-zhang/Fixup
https://github.com/hongyi-zhang/mixup
https://github.com/horse007666/ResNet
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hsd1503/resnet1d
https://github.com/hsinyilin19/resnetvae
https://github.com/hu-my/taskattributedistance
https://github.com/huawei-noah/kd-nlp
https://github.com/huggingface/transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/hun1204/DeepLearning_Visualization_Basic
https://github.com/hunto/dist_kd
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huyvd7/huyvd7
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hyang0129/foodclassapp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hyeonseobnam/sagnet
https://github.com/hyeonseobnam/style-agnostic-networks
https://github.com/hysts/pytorch_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hyunwoongko/resnext-parallel
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/hz2538/iot_JennyGo
https://github.com/iArunava/ResNet
https://github.com/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ildoonet/cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/ildoonet/pytorch-randaugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/ilyak93/GAIN-pytorch
https://github.com/imkoushik22/Walmart-Hackathon
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.