KL-Loss | Bounding Box Regression with Uncertainty for Accurate | Computer Vision library

by yihui-he Python Version: models License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | KL-Loss Summary

KL-Loss is a Python library typically used in Artificial Intelligence, Computer Vision, Pytorch applications. KL-Loss has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Bounding Box Regression with Uncertainty for Accurate Object Detection (CVPR'19)

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KL-Loss has a low active ecosystem.

It has 695 star(s) with 103 fork(s). There are 22 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 28 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of KL-Loss is models

Quality

KL-Loss has 0 bugs and 0 code smells.

Security

KL-Loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

KL-Loss code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

KL-Loss is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

KL-Loss releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed KL-Loss and discovered the below as its top functions. This is intended to give you an instant insight into KL-Loss implemented functionality, and help decide if they suit your requirements.

Add retinanet features to the model
Wrapper for convolution
Visualize a single image
Return a colormap
Convert boxes from cls format
Get class string
Adds RPN outputs to the model
Generates a series of proposal proposals
Generate anchors
Convert city scapes_instance
Add continanet blobs
Add fast RCNN loss
Convert a caffe network
R Evaluate the exposure of a box
Check that the expected results are valid
Add RfcN
Add RPN blobs to FPN
Add mask to image
Process a binary image
Forward the prediction
Argument parser
Calculate the retinanet loss function
Add keypoint outputs to model
Bottleneck bottleneck transformation
Bottleneck convolution bottleneck
Convert heatmaps to keypoints

Get all kandi verified functions for this library.

KL-Loss Key Features

No Key Features are available at this moment for KL-Loss.

KL-Loss Examples and Code Snippets

No Code Snippets are available at this moment for KL-Loss.

Community Discussions

Trending Discussions on KL-Loss

Back propagation from decoder input to encoder output in variational autoencoder

QUESTION

Back propagation from decoder input to encoder output in variational autoencoder

Asked 2020-Aug-15 at 05:15

I am trying to understand VAE in-depth by implementing it by myself and having difficulties when back-propagate losses of the decoder input layer to the encoder output layer.

My encoder network outputs 8 pairs (sigma, mu) which I then combine with the result of a stochastic sampler to produce 8 input values (z) for the decoder network:

...

ANSWER

Answered 2020-Aug-15 at 05:15

The VAE does not use the reconstruction error as the cost objective if you use that the model just turns back into an autoencoder. The VAE uses the variational lower bound and a couple of neat tricks to make it easy to compute.

Referring to the original “auto-encoding variational bayes” paper

The variational lower bound objective is (eq 10):

1/2( d+log(sigmaTsigma) -(muTmu) - (sigmaTsigma)) + log p(x/z)

Where d is number of latent variable, mu and sigma is the output of the encoding neural network used to scale the standard normal samples and z is the encoded sample. p(x/z) is just the decoder probability of generating back the input x.

All the variables in the above equation are completely differentiable and hence can be optimized with gradient descent or any other gradient based optimizer you find in tensorflow

Source https://stackoverflow.com/questions/63258707

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install KL-Loss

Please find installation instructions for Caffe2 and Detectron in INSTALL.md. When installing cocoapi, please use my fork to get AP80 and AP90 scores.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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