Anusua Trivedi | Transfer Learning and Finetuning Deep Convolution Neural Network
PyData SF 2016
Anusua Trivedi | Transfer Learning and Finetuning Deep Convolution Neural Network on Different Domain Specific Images
We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use a pre-trained DCNN on two very different domain specific datasets, and apply fine-tuning to transfer the learned features to the prediction. Our approach improves prediction accuracy on both domain-specific datasets, compared to state-of-the-art approaches.
In this talk, we propose prediction techniques using deep learning on different types of images datasets – medical images and fashion images. We show how to build a generic deep learning model, which could be used with – 1. A fluorescein angiographic eye image to predict Diabetic Retinopathy 2. A fashion image to predict the clothing type in that image We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use an ImageNet pre-trained DCNN and apply fine-tuning to transfer the learned features to the prediction. We use this fine-tuned model on two very different domain specific datasets. Our approach improves prediction accuracy on both domain-specific datasets, compared to state-of-the-art Machine Learning approaches. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
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PyData SF 2016
Anusua Trivedi | Transfer Learning and Finetuning Deep Convolution Neural Network on Different Domain Specific Images
We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use a pre-trained DCNN on two very different domain specific datasets, and apply fine-tuning to transfer the learned features to the prediction. Our approach improves prediction accuracy on both domain-specific datasets, compared to state-of-the-art approaches.
In this talk, we propose prediction techniques using deep learning on different types of images datasets – medical images and fashion images. We show how to build a generic deep learning model, which could be used with – 1. A fluorescein angiographic eye image to predict Diabetic Retinopathy 2. A fashion image to predict the clothing type in that image We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use an ImageNet pre-trained DCNN and apply fine-tuning to transfer the learned features to the prediction. We use this fine-tuned model on two very different domain specific datasets. Our approach improves prediction accuracy on both domain-specific datasets, compared to state-of-the-art Machine Learning approaches. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
