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/fabio-sim/Depth-Anything-ONNX
https://github.com/fabio-sim/LightGlue-ONNX
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
https://github.com/fabxy/accessibilityCV
https://github.com/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/ClassyVision
https://github.com/facebookresearch/ClassyVision/tree/master/examples/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/facebookresearch/anli
https://github.com/facebookresearch/augmentation-corruption
https://github.com/facebookresearch/detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/facebookresearch/domainbed_measures
https://github.com/facebookresearch/fair_self_supervision_benchmark
https://github.com/facebookresearch/habitat-challenge
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/highrescanopyheight
https://github.com/facebookresearch/meta-ot
https://github.com/facebookresearch/multimodal
https://github.com/facebookresearch/ppuda
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facebookresearch/vip-mae
https://github.com/facebookresearch/w2ot
https://github.com/facebookresearch/xformers
https://github.com/facundoq/transformational_measures_experiments
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fairfl/FCFL
https://github.com/fairfl/FUEL
https://github.com/faroukmokhtar/DESY
https://github.com/faroukmokhtar/DESYproject
https://github.com/faroukmokhtar/GradProject
https://github.com/fastai/fastai
https://github.com/fastforwardlabs/causality-for-ml
https://github.com/faustomorales/vit-keras
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/fchollet/deep-learning-models
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.