https://github.com/niranjana98/Image-Classification
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/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/northeastsquare/effficientnet
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nttcslab/dcase2023_task2_evaluator
https://github.com/nuoaier/BaiduStar2020-18-Competition-Solution
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/oddskool/invariant_risk_minimization_reproduced
https://github.com/oellop/facial_expression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ofa-sys/chinese-clip
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/oneflow-inc/libai
https://github.com/open-edge-platform/geti
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/open-edge-platform/training_extensions
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/open-mmlab/mmclassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmdetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmpretrain
https://github.com/open-mmlab/mmselfsup
https://github.com/openmatch/coco-dr
https://github.com/orparask/VS-Loss
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/osmr/imgclsmob
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oxygen0605/ImageClassification
https://github.com/paddlepaddle/edl
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/paddlepaddle/rocketqa
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
https://github.com/paperswithcode/paperswithcode-client
https://github.com/parshakova/IRM_ICP-IC_ERM
https://github.com/parviagrawal/IdenProf
https://github.com/pat-coady/tiny_imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/patientzero/timage-icann2019
https://github.com/patrick013/Image-Classification-CNN-and-VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/paulblum00/ResNet-Recreation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.