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Model Accuracy and Metrics Accuracy ,Plotting the Normalized Cut loss(soft-cut-net-loss & Reconstructed loss (MSE)

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Basically the model contains Encoder part and Decoder Part so basically the Assignment is to segment unsupervised images using by W-Net Model (The way the paper suggest) , so the output of the model will be

1. Input Image (Original Image )

2. Output of the Encoder part which will be Segmented Image

3. Decoder Part (Which will be the reconstructed image of the Input Image ) so we need to implement the paper b

The paper they develop model called W-Net which is model that the encoder will give you Segmented image while the decoder will reconstructor the input image (original image ) So here is the task using by code you need to do

1. You need to make sure that the model will give good result in this way -- Input Image ===> Segmented Image ==> Reconstructed Image

2. Model Accuracy and Metrics Accuracy ,Plotting the Normalized Cut loss(soft-cut-net-loss & Reconstructed loss (MSE)

3. Latent Vector of segmented image (you have to extract latent vector or embedding of the segmented image while you are training your model )

4. Finalize the result Paper Link :- https://arxiv.org/abs/1711.08506

 

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