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Saliency system to model the behaviour of human eye gaze using deep learning. The model uses pre-trained VGG like backbone network and upsamples the high level features extracted from various intermediate layers using bilinear interpolation.

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nilesh0109/CV2_SoSe_19

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CV2_SoSe_19

Repo for maintaining CV2 coursework and project

Build a saliency prediction system using the 1200 training saliency images. The architecture used is similar to ML-Net.

Training a simple Neural network for linear regression task.

Training a simple convolutional neural network on CIFAR-10 dataset.

Transfer Learning using VGG16(Trained on ImageNet 1000 classes) to train the classifer on CIFAR-10 classes.

Training the CNN for saliency output(essentially image output). using the ML-Net weighted loss function for efficient learning.

Add Regularization in the model created in ex5 to avoid overfitting

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Saliency system to model the behaviour of human eye gaze using deep learning. The model uses pre-trained VGG like backbone network and upsamples the high level features extracted from various intermediate layers using bilinear interpolation.

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