https://github.com/haaz77/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Domain Adaptation
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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/haiyang-w/git
https://github.com/hajohajo/TrainingFrameworkTrackDNN
What is the Effect of Importance Weighting in Deep Learning?
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hamedrab/forec
https://github.com/hanfried/hanfried-bookmarks
https://github.com/hanminghao/mscpt
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
https://github.com/hanqingguo/GE2E
https://github.com/haomo-ai/superfusion
https://github.com/haonanguo/remote-sensing-chatgpt
https://github.com/happen2me/adda_pytorch
https://github.com/harisankar95/AdvStyle
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
https://github.com/harsh-99/SCL
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
https://github.com/hasanjawad001/cglearn
https://github.com/hazyresearch/ama_prompting
https://github.com/hcygeorge/aoi_defect_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/healthx-lab/biomedcoop
https://github.com/hellowangqian/scs-lp-pda
https://github.com/henriquemeloo/cat-dog-image-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hershd23/ObjectLocalizer
https://github.com/hh-xiaohu/Image-augementation-pytorch
https://github.com/hhenryd/tap
https://github.com/hhhaaahhhaa/asr-tta
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech
https://github.com/hhl60492/deep-kernel-transfer
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
https://github.com/hieu2906090/deep-learning-in-js
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hilberthit/gpt-3
https://github.com/hitachi-rd-cv/MILA
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
https://github.com/hitchingsh/ml-examples
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/hiyouga/easyr1
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/hkbu-vscomputing/2022_mm_dmae-mocap
https://github.com/hmichaeli/alias_free_convnets
https://github.com/hojjat-mokhtarabadi/promptsource
https://github.com/holdfire/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/holdfire/FAS
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.