Here the authors propose an adversarial contextual model for detecting moving objects in images.
A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible.
The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning.
This method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets.
Paper #arxiv link : https://lnkd.in/dhCxbik
#machinelearning #deeplearning
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible.
The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning.
This method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets.
Paper #arxiv link : https://lnkd.in/dhCxbik
#machinelearning #deeplearning
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
Amazing project success for #DeepLearning for #Radiologists
This CNN model for breast cancer did screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images).
Accuracy? ~ 90% in predicting whether there is a cancer in the breast, when tested on the screening population.
It was a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels.
Paper on #ArXiv https://lnkd.in/ggj5Z6W
Code: https://lnkd.in/gScbpUs
Explanation: https://lnkd.in/gfa9gzM
#ai #deeplearning #radiology #model #breast #mammography
✴️ @AI_Python_EN
This CNN model for breast cancer did screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images).
Accuracy? ~ 90% in predicting whether there is a cancer in the breast, when tested on the screening population.
It was a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels.
Paper on #ArXiv https://lnkd.in/ggj5Z6W
Code: https://lnkd.in/gScbpUs
Explanation: https://lnkd.in/gfa9gzM
#ai #deeplearning #radiology #model #breast #mammography
✴️ @AI_Python_EN