loly0ss

loly0ss t1_jdloewa wrote

Reply to [D] Simple Questions Thread by AutoModerator

Hello everyone,

I had a very ignorant question which I’m trying to find an answer too but i still couldn’t find it.

In terms of the deep learning model in supervised segmentation vs semi-superised segmentation.

Is the model itself the same in both cases, for example using Unet++ for both? And the only diffference comes during training where we use psuedo-labels for example for semi-supervised segmentation?

Or is the model different when it comes between supervised vs semi-supervised segmentation?

Thank you!

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loly0ss t1_j0zjhsb wrote

Reply to [D] Simple Questions Thread by AutoModerator

Hello everyone!

I had a quick question regarding the KL divergence loss as while I'm researching I have seen numerous different implementations. The two most commmon are these two. However, while look at the mathematical equation, I'm not sure if mean should be included.

KL_loss = -0.5 * torch.sum(1 + torch.log(sigma**2) - mean**2 - sigma**2)

OR

KL_loss = -0.5 * torch.sum(1 + torch.log(sigma**2) - mean**2 - sigma**2)

KL_loss = torch.mean(KL_loss)

Thank you!

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loly0ss t1_ix4c4ix wrote

Reply to comment by Conquerer_Aegon in [D] Simple Questions Thread by AutoModerator

Yeah I've trid with no hidden layers and 2 hidden layers still the same. I've also tried Relu and softmax btu sigmoid was better. It's the mnist dataset, I'm trying to predict if the label is 1 or not 1. Since labels of 1 are 10% of the dataset. I reduced the dataset to around 40/60 , so 40% are labeled one and 60% are not ones, which I encoded them to 0.

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loly0ss t1_ix40v2u wrote

Reply to comment by Conquerer_Aegon in [D] Simple Questions Thread by AutoModerator

I have sigmoid in all hidden layers and output but it seems the model is only predicting one class. I tried balancing the datset, changing learning rate, shuffled data and iteration number and weight initilization yet still wrong :(

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loly0ss t1_ix1xo8r wrote

Reply to [D] Simple Questions Thread by AutoModerator

I'm using sigmoid function for binary classification. However I'm using at each layer. For binary classifaction is it better to ouse sigmoid only at the output layer?

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