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/szacho/augmix-tf
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/t0930198/backup
https://github.com/tachitachi/GradientReversal
https://github.com/tahmid0007/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tajamul21/fate
https://github.com/taniokah/liarr2017
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tanjeffreyz/deep-residual-learning
https://github.com/tanmaypandey7/wheat-detection
https://github.com/tarikdgny/Optical_Coherence_Tomography
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tattaka/Antialiased-CNNs-Converter-PyTorch
https://github.com/tcheung99/ResNet_MiniProject
https://github.com/teang1995/satellite_image_task
https://github.com/tensorflow/models/tree/master/research/audioset
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/autoaugment
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/domain_adaptation
https://github.com/tensorflow/models/tree/master/research/slim
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/tpu/tree/master/models/official/detection
https://github.com/tensorpack/tensorpack
https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet
https://github.com/the-super-toys/glimpse-models
https://github.com/theziqi/dccc
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/three0-s/MAE-keras
https://github.com/thughost2/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/thuml/Transfer-Learning-Library
https://github.com/tiagoCuervo/JapaNet
https://github.com/tidbcloud/chat2query_bench
Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models
https://github.com/timH6502/VisionTransformer-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/timeseriesAI/tsai
https://github.com/timgaripov/asa
https://github.com/tingxueronghua/pytorch-classification-advprop
https://github.com/tintn/vision-transformer-from-scratch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tjdhg456/QualNet-Pytorch
https://github.com/tjujianyu/rrl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.