https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 58
- 보존된 평가 기록
- 391
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Zasumn/Double_Net_Distillation
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhouDaShan123/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/a1600012888/YOPO-You-Only-Propagate-Once
https://github.com/aHapBean/PCP-MAE
https://github.com/abaietto/neural_ode_classification
https://github.com/abcp4/DAPytorch
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/abhaskumarsinha/Corpus2GPT
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhuse/cyclic-cosine-decay
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
https://github.com/abhuse/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/acmiyaguchi/birdclef-2022
https://github.com/aczajka/iris-recognition---pm-diseased-human-driven-bsif
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
https://github.com/adam-mehdi/MuarAugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/adldotori/ResNet
https://github.com/adnanalam53/cycleGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/adriaciurana/exploration-stylegan-nada
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
https://github.com/adricarda/AT-DT-Pytorch-implementation
https://github.com/affjljoo3581/deit3-jax
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ahmad0790/svhn-multi-digit-address-recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmed-alllam/Equinox/blob/main/examples/vision_transformer.ipynb
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ahmedbhna/VGG_Paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ai21labs/lm-evaluation
https://github.com/aijianiula0601/ge2eloss-svf
https://github.com/airplane2230/keras_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/aizhanti/jarunc
https://github.com/ajaystar8/PDRUNet-PyTorch
https://github.com/ajdillhoff/simgan-pytorch
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ajfisch/few-shot-cp
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
https://github.com/ajgallego/dann
Incremental Unsupervised Domain-Adversarial Training of Neural Networks
https://github.com/ajithvallabai/getsetgo_keras-beginner
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akamaster/pytorch_resnet_cifar10
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.