https://github.com/Gitikameher/PixelDA
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
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/Glp91/fire-detection
https://github.com/Godot-yry/VGG16_mindspore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gojay001/toolkit-DeepLearning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/HAHA-DL/MLDG
Learning to Generalize: Meta-Learning for Domain Generalization
https://github.com/HHTseng/video-classification
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HUSTSYJ/DA_dahazing
https://github.com/Halesu/4th-ML100Days
https://github.com/Hanson0910/MNNSuperGlue
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/HaohanWang/ImageNet-Sketch
Learning Robust Global Representations by Penalizing Local Predictive Power
https://github.com/HaoyueBaiZJU/DecAug
https://github.com/HaoyueBaiZJU/NAS-OoD
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/HarryVolek/PyTorch_Speaker_Verification
https://github.com/Hazelsuko07/InstaHide
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/He-Zhenwei/MAF
Multi-adversarial Faster-RCNN for Unrestricted Object Detection
https://github.com/Henrygwb/conditional-domine-transfer-network
https://github.com/HiGal/Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/Hong-Hyun-Seung/Mask_Classification
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/Hongbin98/MonoTTA
Fully Test-Time Adaptation for Monocular 3D Object Detection
https://github.com/HsinYingLee/MDMM
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/HuikaiShao/JPFA
A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/VIT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/IBM/IRM-games
Treatment Effect Estimation using Invariant Risk Minimization
https://github.com/IBM/NeuronAlignment
https://github.com/IBM/retrieve-write-slot-filling
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
https://github.com/IMvision12/keras-vision-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/INK-USC/DualRE
https://github.com/IamWangYunKai/DG-TrajGen
Domain Generalization for Vision-based Driving Trajectory Generation
https://github.com/IcarusWizard/MAE
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.