https://github.com/namakemono/kaggle-alaska2-image-steganalysis
Improved Regularization of Convolutional Neural Networks with Cutout
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 58
- 보존된 평가 기록
- 391
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/nanzhaogang/contrib/tree/master/application/learning-an-explicit-hyperparameter-prediction-function-conditioned-on-tasks
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/narumiruna/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/nasa-impact/hls-foundation-os
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nateraw/lightning-vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nathanieljevans/DVGS
https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness
https://github.com/natureyoo/continualtta_objectdetection
What, How, and When Should Object Detectors Update in Continually Changing Test Domains?
https://github.com/naver-ai/pflayer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nchungvh/dlcl
https://github.com/ncoop57/gpt-code-clippy
https://github.com/nctumllab/huang-ching-wei
https://github.com/ndb796/pytorch-adversarial-training-cifar
https://github.com/neerajvashistha/vgg16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/neerakara/TTA_prostate
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
https://github.com/neulab/knn-transformers
Generalization through Memorization: Nearest Neighbor Language Models
https://github.com/neuralmagic/lm-evaluation-harness
https://github.com/neverCV/Baseline_FSL
https://github.com/ngonthier/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicaogr/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicku-a/Weather_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicolalandro/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicolas-dufour/bird-classification-recvis
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/nidolow/image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nigelyaoj/self-supervised-amodal-video-object-segmentation
https://github.com/nikolaev-n/CocktailPartySpeakerRecognition
https://github.com/nikolaevs92/DL-articles-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nima1999nikkhah/ViT-Hybrid
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nimiew/Grab-Computer-Vision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/niranjana98/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/njunlp/knn-box
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
https://github.com/nlx-group/overlapy
https://github.com/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/northeastsquare/effficientnet
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
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.