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6 votes

Why diffusion model always use U-Net?

I don't have a definitive answer but I'd state my intuitions anyways: Diffusion models are highly related to the idea of stacked denoising autoencoders [Kumar et al. (2014)]. Additionally, U-Net-like ...
Chillston's user avatar
  • 1,748
4 votes

Learning an identity function with convolutional networks

Learning the identity function is not trivial at all. The main reason is that the identity function is linear, and a neural network try to approximate it in a non linear fashion. Non linear ...
Edoardo Guerriero's user avatar
3 votes

What are the best algorithms for image segmentation tasks?

U-Net and U-Net inspired architectures have been quite popular in the medical image-related tasks ever since it was first introduced. There have been several improved versions of U-Net designed for ...
Tanmay Agrawal's user avatar
3 votes
Accepted

What is the role of skip connections in U-Net?

You should checkout the original resnet paper which popularized skip connections, or shortcut connections, in the modern literature. See Related Work Section 2 in the paper. Basically though, ...
juicedatom's user avatar
3 votes
Accepted

How to perform latent space Interpolation between two images?

You need more training images. Far more, at least a few hundred, with variations. The latent space has no meaningful form to it when you train with just two end points. The decoder will have no ...
Neil Slater's user avatar
  • 32.7k
3 votes

What is the use of the regular convolutional layer in expansion path of U-Net?

The point is that in the expansive path you have two forms of information: the information from the contracting path, which includes all high-level features extracted from the original image. the ...
Djib2011's user avatar
  • 3,193
2 votes

What are some good alternatives to U-Net for biomedical image segmentation?

Hey i am working on my Bachelor thesis at the moment and use UNET in combination with a GAN for image segmentation. I spend the last 5 months on that, so on my tests, the new approach of januar 2020, ...
Paul Higazi's user avatar
2 votes
Accepted

Why everyone is using CNN for image segmentation?

CNN is used since it is effectively an optimized use case for dealing with image data. CNN effectively automatically extracts features from images. Other techniques are more likely to not take full ...
Ryan Marinelli's user avatar
2 votes
Accepted

What does the "number of channels" correspond to in U-Net?

In this example you have a gray scale image of size 572x572 and 1 (gray) channel. The first convolution operation consists of 64 filters of size 3x3 and 1 channel per filter. The channel of the ...
legammler's user avatar
2 votes
Accepted

How should I incorporate numerical and categorical data as part of the inputs to the U-net for semantic segmentation?

What you want to do is called multi-task learning. Here's what you do: Create a second Input. Attach it to 1D CNN (2-3 layers), so it aggregates this tabular information. Concatenate this feature ...
Abhishek Verma's user avatar
1 vote

Why diffusion model always use U-Net?

They do not. Even "the" seminal paper does not (it was put on axiv before "U-Net" was a thing :).
Warpig's user avatar
  • 11
1 vote
Accepted

3D Unet gives "output size is too small" error

Why are you using 3D convolution/pooling if you input a 2D image ? The expected input size should be BxCxDxHxW for a 3D convolution, not BxCxHxW . In your case, C=1 because you said you only have one ...
Lelouch's user avatar
  • 216
1 vote

Image segmentation with varying resolution

Usually the solution would be just to add padding, and use a model that is trained to handle padding. In other words, fix a resolution, and then downscale the image you are handling to fit in that ...
Alberto's user avatar
  • 2,293
1 vote

Which models can be applied recursively?

You mentioned dynamics, so I assume you have some kind of vectorised state $S$ and the model should output $S'$ in the same space. A transformer will do this. A special case of UNet which outputs the ...
Venna Banana's user avatar
1 vote

Use of Mask in U Net for plant disease detection

Here is what I understand you to be asking Given: Task: is plant disease detection Solution domain: is deep learning and computer vision Tool: is U-net Activity: image segmentation Questions: Why ...
EngrStudent's user avatar
1 vote

How does the skip connection match its dimension to the same layer in the expansive path?

Output of each layer in the upscaling block is of the same size as the input of corresponding convolution layer in the downscaling block after cropping the input's feature maps. This is how the ...
ashutosh singh's user avatar
1 vote

Unet Overfitting for binary segmentation of fake images

I do not understand why you say that your model is overfitting. An overfit occurs when the validation loss start increasing after diminishing. Here it seems that your model has reaches its potential ...
bguetarni's user avatar
1 vote

Unet Overfitting for binary segmentation of fake images

Data augmentations is usually done on the fly during training, meaning before each you apply the random augmentation for the entire dataset, because of the randomness there will be different ...
spb's user avatar
  • 31
1 vote
Accepted

How to use mixed data for image segmentation?

You can try doing image segmentation the traditional way, just using the image data. If you want to use the non-image data, then, you can introduce classification as another task for your network. It ...
Abhishek Verma's user avatar
1 vote
Accepted

Why does my model not improve when training with mini-batch gradient descent, while it does with Adam?

Well, some time ago I also faced the same issue in the semantic segmentation task. Batch normalization is expected to improve convergence, because the normalization of activations prevents the ...
spiridon_the_sun_rotator's user avatar
1 vote
Accepted

Have I understood the loss function from the original U-Net paper correctly?

Yes, $E$ is the cross-entropy function and a direct generalization of the binary case. For the binary case, probability to belong to the class $1$ is given by a sigmoid function $\sigma(x)$ of the ...
spiridon_the_sun_rotator's user avatar
1 vote

Semantic segmentation CNN outputs all zeroes

Similar to other answers, I don't know Matlab that well but you could try the following steps to debug your problem. Make sure you can overfit to a single instance from your dataset, pull out a single ...
juicedatom's user avatar
1 vote
Accepted

Validation accuracy higher than training accurarcy

I am answering my own question here. The only additional thing that I found was that the average accuracy across a batch of data was slightly higher if the amount of samples in the batch was lower. So ...
Lis Louise's user avatar
1 vote

How to quickly change hand-drawn shapes to symmetrical polished shapes?

From how it looks, the most reliable method to try out is using Hough transform. The Hough transform can be used to detect e.g. lines and circles in images (depending on which variant you are using; ...
Daniel B.'s user avatar
  • 825
1 vote

What are the best algorithms for image segmentation tasks?

You can find leaderboards as well as code at this address. For now, HRNetV2 leads the game. The U-Net architecture is part of a broad family of network architectures that aggregate multi-scale ...
Louis Lac's user avatar
  • 318

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