https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/caraj7/t2i-r1
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO
https://github.com/cardwing/codes-for-pvkd
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carpedm20/simulated-unsupervised-tensorflow
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/carrenD/Med-CMDA
https://github.com/carrenD/Medical-Cross-Modality-Domain-Adaptation
https://github.com/carsonyang518/aaai24-aes-afg
https://github.com/casia-iva-lab/pass-reid
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/caskcsg/CoT-MAE
Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
https://github.com/caskcsg/ir
Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
https://github.com/cassiePython/cddfm3d
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
https://github.com/cgebbe/kaggle_pku-autonomous-driving
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/chamwen/JPDA
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation
https://github.com/changewOw/Cutout-numpy
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/charchit7/Using_Transoformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/cheng-Ye/daguan-competition-master
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/chenhao2345/ICE
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/chenhao2345/gcl-extended
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/chenkang121/DANN
https://github.com/chenller/mmseg-extension
TransNeXt: Robust Foveal Visual Perception for Vision Transformers
https://github.com/chenyang4/resmooth
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/cherise215/maxstyle
https://github.com/chi-chi-zx/L2C
https://github.com/chia-hsuan-lee/spoken-squad
https://github.com/chiahsuan156/Spoken-SQuAD
https://github.com/chldydgh4687/2020-1.VideoCaptioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chncwang/InsNet
Unseen Target Stance Detection with Adversarial Domain Generalization
https://github.com/chosj95/PFPNet.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chrhenning/hypercl
https://github.com/chrisqqq123/FA-Dist-EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/christiansafka/img2vec
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.