Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 21
- 보존된 평가 기록
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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/deepquestai/fire-smoke-dataset
https://github.com/deftruth/torchlm
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
https://github.com/demul/ResidualNet
https://github.com/denizyuret/playground
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/deontaepharr/Residual-Attention-Network
https://github.com/developer0hye/SKNet-PyTorch
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/dineshdaultani/DiffAUD
https://github.com/dishen12/Cuout_modify
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/dishen12/py03
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/dispink/xpt
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/djwhsdj/vw-sdk
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/dmizr/phuber
https://github.com/dnddnjs/pytorch-cifar10
https://github.com/doantienthongbku/Implementation-patchnet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/domainadaptation/salad
https://github.com/dominickrei/limited-data-vits
https://github.com/dongdong69/MixAugmentation
https://github.com/donghao51/aeo
https://github.com/dongkyuk/ConvNext-tensorflow
https://github.com/dravenww/curated-article
https://github.com/drumpt/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/dslisleedh/MLP_based_models-tensorflow2/blob/master/gfnet.py
https://github.com/dssbxx/k_labels_resnet50
https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI
https://github.com/duyhominhnguyen/LVM-Med
https://github.com/dv-fenix/Domain-Adaptation
https://github.com/dvlab-research/VisionReasoner
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/dvlab-research/imbalanced-learning
https://github.com/dvlab-research/rescom
https://github.com/dzhaoxd/seco
Source-Free Open Compound Domain Adaptation in Semantic Segmentation
https://github.com/eaguaida/TF_EnsNet-
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elasticdeeplearning/edl
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/emla2805/vision-transformer
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.