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
- 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/vietdhoang/resnet-18
https://github.com/vill-lab/2024-aaai-hpt
https://github.com/vinay-EIP/EIP-session-5-assignment
https://github.com/vineeths96/Gradient-Compression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vineeths96/heterogeneous-systems
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vinod377/STN-OCR
https://github.com/vinod377/STN-OCR-Tensorflow
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer
https://github.com/vinuni-vishc/few-shot-transformer
https://github.com/virajprabhu/pacmac
https://github.com/vita-epfl/rock-pytorch
https://github.com/vladthesav/MoldAI
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/vlievin/Unet
https://github.com/vwegn/dm
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wangermeng2021/FastClassification
https://github.com/wangguanan/light-reid
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wangguangyuan/ClusterContrast
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/wangsr126/mae-lite
https://github.com/wangyi111/international-archaeology-ai-challenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wangyuan249/Mymmt767
https://github.com/wangyz1608/knowledge-distillation-via-nd
https://github.com/wasilone11/ICPR-RIP-2024
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/waterdisappear/nudt4mstar
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/waychin-weiqin/itsa
https://github.com/weixin-liang/metashift
https://github.com/wen227/Pokemon_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wenkehuang/marsfl
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
https://github.com/wenxinxu/resnet-in-tensorflow
https://github.com/wenxinxu/resnet_in_tensorflow
https://github.com/wfwf10/feature-based-federated-transfer-learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wildphoton/RandConv
Robust and Generalizable Visual Representation Learning via Random Convolutions
https://github.com/williamwang96/Almond-Recognition
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/winycg/MCL-OKD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wkostuch/wild-style
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.