I am implementing a training loop that can be used with Auxiliary Classifier. So what is an Auxiliary Classifier?

Auxiliary Classifier are the ones in which we take the outputs of layers of some previous layers along with the final outputs and compare it with the targets and calculate a loss based on both the outputs from the final layer as well as the previous layer.

How does this help?
I think before even me saying how this is going to be helpful, I think this intuitively gives an idea of how is this going to aid the training process, I got so freaking excited when I came to know about this.

So, How does this help?
- Solves the gradient Vanishing problem
- Low-level features get more and more accurate and thus making the model more and more accurate.
- This also acts as regularization, it kind of can be thought as putting some constraints on the model which help in regularization.

I am not sure which paper first introduced Auxiliary Approaches, but I am trying to train an FCN using this, let's see how this aids the process :)
Maybe the Inception paper.

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✴️ @AI_Python_EN

Using Neural Networks to perform makeup.
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https://medium.com/mathematical-beauty/deep-learning-for-cosmetics-2d1b427bbfa2

✴️ @AI_Python_EN

Releasing STEAL, a new semantic boundary detector that significantly outperforms past work. Use STEAL to refine segmentation datasets, and train better segmentation models!
paper:https://arxiv.org/abs/1904.07934
code:https://github.com/nv-tlabs/STEAL
#computervision
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Capturing Context in Emotion AI: Innovations in Multimodal Video Sentiment Analysis
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1000x Faster Data Augmentation

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Dr. Andrew Fitzgibbon is an expert in 3D #computervision and graphics. Discover work on body- and hand-tracking for tech like Kinect and HoloLens and hear how research on dolphins helped build models for the human hand:
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How to make a pizza: Learning a compositional layer-based GAN model. Or “MIT’s AI learns to Become Pizza Guru. All pizza design will soon be automated. ”
https://arxiv.org/abs/1906.02839
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✴️ @AI_Python_EN

Check out Scene Representation Networks:
https://youtu.be/6vMEBWD8O20
new continuous 3D-aware scene representation reconstructs appearance and geometry just from posed images, generalizes across scenes for single-shot reconstruction, and naturally handles non-rigid deformation!
https://arxiv.org/abs/1906.01618
#computervision

✴️ @AI_Python_EN

All the datasets (there are a lot) released at #cvpr2019 are now indexed in
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✴️ @AI_Python_EN

TensorFlow 2.0 Beta has just been released!! This time, I am a big fan. The new version is so good, so easy & intuitive, and game changing compared to the previous TensorFlow 1 versions. It has such massive value that I decided to make a huge course on TensorFlow 2.0, covering most of the useful models in Deep Learning and Artificial Intelligence. Seriously this is one of the most complete guides I’ve ever made: inside we implement ANNs, CNNs, RNNs, Deep Q-Learning, Transfer Learning, Fine Tuning, APIs for Mobile Apps, Computer Vision, Deep NLP, Data Validation, TensorFlow Extended and even Distributed Training handling multiple GPUs, all that in TensorFlow 2.0!

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Convolutional #NeuralNetworks (CNN) for Image Classification — a step by step illustrated tutorial: https://dy.si/hMqCH
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✴️ @AI_Python_EN

VideoBERT: A Joint Model for Video and Language Representation Learning

Sun et al.: https://lnkd.in/ek7MYKP

#ComputerVision #PatternRecognition #ArtificialIntelligence

✴️ @AI_Python_EN

Prof. Chris Manning, Director of StanfordAILab & founder of Stanfordnlp, shared inspiring thoughts on research trends and challenges in #computervision and #NLP at #CVPR2019. View full interview:

http://bit.ly/2KR21hO

✴️ @AI_Python_EN

10 new posts on datahacker.rs.
Introduction to #ComputerVision using #OpenCV (#Python and #C++)

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Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
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.

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ICCV 2019 | Best Paper Award: SinGAN: Learning a Generative Model from a Single Natural Image
https://lnkd.in/fS3ZBAP

ICCV 2019 | Best Student Paper Award: PLMP — Point-Line Minimal Problems in Complete Multi-View Visibility
https://lnkd.in/f7CDuq2

ICCV 2019 | Best Paper Honorable Mentions
Paper: Asynchronous Single-Photon 3D Imaging
https://lnkd.in/fMpQPCj

Paper: Specifying Object Attributes and Relations in Interactive Scene Generation
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New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0

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Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.

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Free 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!

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