fabiochiu/medium-articles huggingface dataset:
[Paper] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Efficientnet, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Image Classification)

Model Size vs. ImageNet Accuracy.

In this story, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (EfficientNet), by Google Research, Brain Team, is presented. In this paper:

Model scaling is systematically studied to carefully balance network depth, width, and resolution that can lead to better performance.

is systematically studied to that can lead to better performance. An effective compound coefficient is proposed to uniformly scale all dimensions of depth/width/resolution.

is proposed to With neural architecture search (NAS), EfficientNet is obtained.

This is a paper in 2019 ICML with over 1100 citations. (Sik-Ho Tsang @ Medium)
[Paper] Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Image Classification)
By: Sik-Ho Tsang
Tags: Dropout, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Image Classification)

In this paper, Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Dropout), by University of Toronto, is shortly presented.

The key idea is to randomly drop units (along with their connections) from the neural network during training.

This prevents units from co-adapting too much.

This is firstly appeared in 2012 arXiv with over 5000 citations. And it is used in AlexNet (2012 NIPS), with over 73000 citations and got the first place in 2012 ImageNet competition. Finally, it is published in 2014 JMLR with over 23000 citations. (Sik-Ho Tsang @ Medium)
[Paper] ShakeDrop: Shakedrop Regularization for Deep Residual Learning (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Artificial Intelligence, Convolutional Network, Regularization, Deep Learning

[Paper] ShakeDrop: Shakedrop Regularization for Deep Residual Learning (Image Classification)

ShakeDrop: converge to a better minimum

In this story, ShakeDrop Regularization for Deep Residual Learning (ShakeDrop), by Osaka Prefecture University, and Preferred Networks, Inc., is shortly presented. In this paper:

ShakeDrop is proposed which is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet (WRN), and PyramidNet.

This is a paper in 2019 IEEE ACCESS with over 40 citations, where ACCESS is an open access journal with high impact factor of 3.745. (Sik-Ho Tsang @ Medium)
[Paper] DropBlock: A Regularization Method for Convolutional Networks (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, AI, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] DropBlock: A Regularization Method for Convolutional Networks (Image Classification)

In this story, DropBlock: A regularization method for convolutional networks (DropBlock), by Google Brain, is shortly presented. In this paper:

DropBlock, a form of structured Dropout, is proposed where units in a contiguous region of a feature map are dropped together.

Applying DropBlock in skip connections in addition to the convolution layers increases the accuracy.

This is a paper in 2018 NeurIPS with over 200 citations. (Sik-Ho Tsang @ Medium)
Processamento básico de imagens radiográficas utilizando OpenCV
By: Eduardo Farina
Tags: Radiologia, Data Preprocessing, Convolutional Network

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
[Paper] FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (Image Classification)
By: Sik-Ho Tsang
Tags: Neuralarchitecturesearch, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (Image Classification)

Differentiable neural architecture search (DNAS) for ConvNet design

In this paper, FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (FBNet), by UC Berkeley, Princeton University, and Facebook Inc, is presented. In this paper:

A differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures .

framework that uses gradient-based methods to . FBNets (Facebook-Berkeley-Nets), a family of models discovered by DNAS outperforms SOTA approaches.

This is a paper in 2019 CVPR with over 300 citations. (Sik-Ho Tsang @ Medium)
[Paper] NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Artificial Intelligence, Convolutional Network, Mobilenet, Deep Learning

[Paper] NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (Image Classification)

NetAdapt

In this story, NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (NetAdapt), by Massachusetts Institute of Technology (MIT), and Google Inc., is presented. In this paper:

By using NetAdapt. the pre-trained network is simplified progressively until the resource budget is met while maximizing the accuracy.

This is a paper in 2018 ECCV with over 190 citations. (Sik-Ho Tsang @ Medium)
[Paper] ProxylessNAS: Direct Neural Architecture Search on Target Task (Image Classification)
By: Sik-Ho Tsang
Tags: Neuralarchitecturesearch, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] ProxylessNAS: Direct Neural Architecture Search on Target Task (Image Classification)

Left: Conventional NAS, Right: ProxylessNAS

In this story, ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (ProxylessNAS), by Massachusetts Institute of Technology (MIT), is presented.

In conventional NAS:

The prohibitive computational demand (e.g. 10⁴ GPU hours) makes it difficult to directly search the architectures on large-scale tasks such as ImageNet.

such as ImageNet. They need to utilize proxy tasks, such as training on a smaller dataset , then use the learned cells on large-scale target tasks.

, then use the learned cells on large-scale target tasks. However, architectures optimized on proxy tasks are not guaranteed to be optimal on the target task.

In this paper:

ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms, to address the high memory consumption issue of differentiable NAS and reduce the computational cost.

This is a paper in 2019 ICML with over 500 citations. (Sik-Ho Tsang @ Medium)
AI Can See You. Here’s How:
By: Eason Wu
Tags: AI, Artificial Intelligence, Convolutional Network, Technology, Computer Vision

How do you train a CNN?

Training a CNN is quite complicated, but I’ll try to keep it simple! I’ll be explaining it in a few sections:

Inputs

Kernels

Pooling

An Example Of A CNN Used To Detect Handwritten Digits (Image by Sumit Saha on Medium)

Inputs

Any neural network needs some sort of input to be able to do anything. Since we’re using CNNs for computer vision the most likely form of input will be images or videos. But let’s stick to images for now.

Black and white images

First, the pixels will be put into a 2D tensor, think a grid. Each pixel will be assigned a value from 0–255 depending on how light or dark it is. Darker pixels will be closer to 0 and lighter ones will be closer to 255.

A 5 From The Famous MNIST Dataset With The Pixel Values (Image by Cecelia Shao on Medium)

Obviously, black and white images are much simpler given that this is a simple 2D matrix. Other images, like colour images, are a 3D tensor.

Color images

Color images follow a very similar process to black and white images. As most people know, the three primary colours (for digital) are RGB, or red, blue, and green. This is exactly how colour images are broken down. You can think of it as 3 sheets of coloured film stacked to make one image.

3x3 Colour Image (Image by Brandon Rohrer on KDnuggets)

A colored image should have 3 channels, one for each colour. Collectively, this is also called a tensor. The numbers of channels are like the number of “layers” of a tensor.

Kernels

A kernel is a matrix that is foundational to CNNs. They are used for feature extraction in images. There are also many variables in kernels that can be tweaked including size and step.

Size. The size of a kernel just means how tall or large it is, in pixels. These can be adjusted for many reasons, including how specified or generalized you want the next layer to be.

Step. The step of a kernel determines how large of a step the kernel will take. A step of 2 means it will move 2 pixels down each time.

So what do kernels do? As I mentioned before, the kernel is used to extract features from images. You can think of a kernel as a magnifying glass that reports what it sees on each part of an image. The size would determine the size of the lens and the step would determine how much the magnifying glass would move each time.

The 3x3 Orange Square Is A Kernel With A Step Of One (Gif by Sumit Saha on Medium)

The values of a kernel multiply to the values of the pixels of an image in something called element-wise multiplication. The sum of the results from the element-wise multiplication produces a new image, now smaller and more specific to one kind of feature.

In the first few layers of a CNN, the kernels are likely more generalized, like for recognizing lines, then it will be progressively more specific, like recognizing corners, then circles, then ears, then faces.
Cactus image classification using convolutional neural network (CNN) that reaches over 98% accuracy
By: Jonathan C.T. Kuo
Tags: Neural Networks, Convolutional Network, Computer Science, Machine Learning, Classification

The benefit of this method is that it saves all the detail of the image; however, we sometimes lose the edge of the image, and if the image is too small, we need to expand the image with a black background to make it the same size as the others. Losing the edge could be a big problem as it is possible that we cut the cactus out of the image with this technique.

CNN structure & training

The convolutional neural network contains 3 layers of convolutional layers and 2 fully connected layers. Each convolutional layer has a 3 by 3 filter that takes a stride of 2 with an output of 64 nodes. After that, the data travels through the max-pooling layer to prevent overfitting and to extract useful information.

model = Sequential() model.add(Conv2D(64, (3,3), input_shape = X_train.shape[1:])) model.add(Activation(‘relu’)) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(64, (3,3))) model.add(Activation(‘relu’)) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(64, (3,3))) model.add(Activation(‘relu’)) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(64)) model.add(Dense(1)) model.add(Activation(‘sigmoid’)) model.compile(loss=”binary_crossentropy”, optimizer=”adam”, metrics=[‘accuracy’]) history = model.fit(X_train, Y_train, batch_size=32, epochs=10, validation_split=0.1, use_multiprocessing=True) model.save(‘model_48_crop’)

Below is an overview of the model structure.

Summary of the model

We trained the model with 10 epochs, and it showed amazing results. The first accuracy is the training accuracy in the below snippet, and the second one is the validation accuracy. Notice that we used part (10%) of the training set as our validation set before the final prediction.
Image Filters with Convolutions
By: Pooja Mahajan
Tags: Neural Networks, Scipy, Convolutional Network, Python, Deep Learning

Image Filters with Convolutions

In this post, I will discuss about Convolutions and how they act as image filters by implementing convolution operation using a few edge detection kernels.

So let’s start!

What are Convolutions?

Convolutions are mathematical operation on two functions that produces a third function that expresses how one is modified by the other.

To give an example, the first function can be the image and the second function is a matrix sliding over the image(kernel) that results in transforming the input image. Kernel works by multiplying the patch of the image corresponding to the kernel’s size and summing up the result obtained and then sliding over the image to perform the same process again.

Image Source

Thus, Convolution operation can help in extracting features from images with the help of kernels. For more details on Convolutions and Kernels, please refer to my previous blog here.

Image Filters with Convolutions

In order to demonstrate how convolution operation can be used as Image Filters, I have used signal.convolve2d function from the scipy library of Python that outputs convolution result for two 2D arrays.

Importing required libraries

Loading and displaying images

Defining a function to detect features from the image using specified kernel

Detecting horizontal edges

Transformed image after convolution operation using a horizontal edge detector kernel

Detecting vertical edges

Transformed image after convolution operation using a vertical edge detector kernel

Detecting diagonal edges

Transformed image after convolution operation using a diagonal detector kernel

This was a quick demo to show when a kernel convolves over an image it can act as a filter to extract a particular feature from it.

You can find the corresponding codes here.
[Paper] P3D: Pseudo-3D Residual Networks (Video Classification & Action Recognition)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Deep Learning, Video Classification, Resnet

[Paper] P3D: Pseudo-3D Residual Networks (Video Classification & Action Recognition)

In this story, Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks (P3D), by University of Science and Technology of China, and Microsoft Research, is briefly presented.

3D CNN is computational and memory expensive.

In this paper:

3×3×3 convolutions are designed as 1×3×3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3×1×1 convolutions to construct temporal connections on adjacent feature maps in time.

are designed as on adjacent feature maps in time. Bottleneck building blocks are built, which formed a network, named Pseudo-3D Residual Net (P3D ResNet).

This is a paper in 2017 ICCV with over 500 citations. (Sik-Ho Tsang @ Medium)
[Paper] DB-CNN: Deep Bilinear Convolutional Neural Network (Image Quality Assessment)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Iqa, Quality Assessment, Deep Learning

In this story, Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network (DB-CNN), by Wuhan University, New York University, and University of Waterloo, is presented. I read this because I recently study IQA/VQA. In this paper:

For synthetic distortions , a CNN is pre-trained to classify image distortion type and level .

, a CNN is pre-trained to . For authentic distortions , a pretrained CNN for image classification is adopted .

, . The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction.

The entire model is fine-tuned on target subject-rated databases.

This is a paper in 2020 TCVST where TCSVT has a high impact factor of 4.133. (Sik-Ho Tsang @ Medium)
[Paper] DARTS: Differentiable Architecture Search (Image Classification)
By: Sik-Ho Tsang
Tags: Neuralarchitecturesearch, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] DARTS: Differentiable Architecture Search (Image Classification)

The normal cell for convolution for CIFAR-10 along epoch.

In this story, DARTS: Differentiable Architecture Search (DARTS), by CMU, and DeepMind, is presented. In this paper:

Architecture search method is based on the continuous relaxation of the architecture representation , allowing efficient search of the architecture using gradient descent .

, efficient search of the architecture using . This is unlike conventional approaches which apply evolution or reinforcement learning, and it is orders of magnitude faster than state-of-the-art non-differentiable techniques.

This is a paper in 2019 ICLR with over 900 citations. (Sik-Ho Tsang @ Medium)
A Complete Guide for Visualising and Understanding Convolutional Networks
By: Anant Kumar
Tags: Deconvolution, Artificial Intelligence, Convolutional Network, Python, Deep Learning

Disclaimer: If you are interested only in the code for DeConvNet, it is present at the end of the article with a ConvNet taken as an example.

Convolutional Networks (ConvNets), introduced by LeCun et al., 1989 have achieved state-of-the-art results in challenging visual classification tasks in recent times.

What caused this renewed interest in ConvNet models?

Availability of Training Sets Significant Increase in Compute Power Novel Model Regularization Techniques

There is a common analogy among practitioners that insights into the internal operation and behavior of these models or the reason how they achieve such good performance is a cumbersome task (if not impossible).

But is this analogy correct?

Without clear understanding of the reason behind architecture and functioning of CNNs (or any other model), these models are reduced to trial-and-error. From a model interpretability standpoint, this is deeply unsatisfactory.

Is it possible to interpret a ConvNet model?

Indeed, it is !

Zeiler et. al.¹ proposed a DeConvNet (Multi-layered DeConvolutional Network). The key features of the proposed DeConvNet are listed below :
[Paper] DeepBIQ: Deep Learning for Blind IQA (Image Quality Assessment)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Iqa, Quality Assessment, Deep Learning

[Paper] DeepBIQ: Deep Learning for Blind IQA (Image Quality Assessment)

Examples from the LIVE In the Wild IQ Chall. DB

In this story, On the use of deep learning for blind image quality assessment (DeepBIQ), by University of Milano-Bicocca, is presented. I read this because I recently study IQA/VQA. In this paper:

Features of subregions are extracted by fine-tuned convolutional neural networks (CNNs) as a generic image description, then input to SVR to regress the image quality scores.

as a generic image description, then input to SVR to regress the image quality scores. The image quality is estimated by average-pooling the scores predicted on multiple subregions of the original image.

of the original image. This proposed approach is named DeepBIQ.

This is a paper in 2017 Springer JSVIP (Signal, Image and Video Processing). (Sik-Ho Tsang @ Medium)
[Paper] SACONVA: Shearlet- and CNN-based NR VQA (Video Quality Assessment)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Vqa, Quality Assessment, Deep Learning

[Paper] SACONVA: Shearlet- and CNN-based NR VQA (Video Quality Assessment)

In this story, No-Reference Video Quality Assessment With 3D Shearlet Transform and Convolutional Neural Networks (SACONVA), by City University of Hong Kong, and Chu Hai College of Higher Education, is briefly presented. This is a paper introduced by colleague when I study on IQA/VQA. In this paper:

SACONVA: Framework Overview

Taking video blocks as input, spatiotemporal features are extracted by 3D shearlet transform.

Then, CNN and logistic regression are concatenated to predict a perceptual quality score.

This is a paper in 2016 TCSVT with over 50 citations where TCSVT has a high impact factor of 4.133. (Sik-Ho Tsang @ Medium)
Significance of Kernel size
By: Anuja Ihare
Tags: Kernel, Convolutional Network, Deep Learning

Before we start with significance of kernel size lets first revisit what kernel really is.

In Fully-connected network every vector element in the input is connected to every hidden unit in first layer. So you connect every unit to every other unit.

So if you have 11x11 image then there would be 121 connections to a single unit and in practice there would be 363 connections. Since every image has 3 channels.

In locally-connected network we use a kernel/filter. Kernel is a part of image that a unit can see. So we can say that kernel is like a window. But here the windows may differ i.e the weight of the kernel may not be same.

Convolution network is similar to locally connected network except that there is weight sharing in kernel.

The kernel keep sliding across the image to generate output. The generated output is called feature map.

How does convolution work?

The intent of convolution is to encode source data matrix i.e entire image in terms of a filter or kernel. More specifically, we are trying to encode the pixels in the neighborhood of anchor/source pixels

Therefore, whatever this number of pixels maybe, the length of each side of our symmetrically shaped kernel is 2*n+1 (each side of the anchor + the anchor pixel), and therefore filter/kernels are always odd sized.

What about the boundary/edges ?

There are different ways of dealing with this:

Ignore it Add zero padding Mirror reflect the image

Typically, the pooling operation (average pooling or max pooling) will remove your boundary artifacts anyway.

If you are not going to compute an inverse operation, i.e. de-convolution, and are not interested in perfect reconstruction of original image, then you don’t care about either loss of information or injection of noise due to the boundary problem.

Why kernel size is odd ?

For an odd-sized filter, all the previous layer pixels would be symmetrical around the output pixel. Without this symmetry, we will have to account for distortions across the layers.

Why not even sized kernel ?

It is possible to use even size kernel but you might have to suffer with aliasing error.

Aliasing is distortion caused in original input. Mostly when we down sample in signal processing this is solved by low-pass filtering but this cannot be inserted in deep networks as it degrades performance.

Why 3x3 ?

The number of parameters grows quadratically with kernel size. This makes big convolution kernels not cost efficient enough.

Limiting the number of parameters, we are limiting the number of unrelated features possible.

This forces Machine Learning algorithm to learn features common to different situations and so to generalize better.

Hence common choice is to keep the kernel size at 3x3 or 5x5.

We would like to use smaller odd-sized kernel filters. But, 1x1 is eliminated from the list candidate optimal filter sizes as the features extracted would be fine grained and local, with no information from the neighboring pixels. Also, it is not really doing any useful feature extraction
Investigating State & Corporate Violence with ML & CV
By: Jerry Efremides
Tags: Human Rights, Police Brutality, Convolutional Network, Data Science, Computer Vision

Forensic Architecture’s use of computer vision & machine learning sheds light on human rights violations and expands the conversation on ethical philanthropy

Building convolutional neural networks (cnn) to classify images on a Jupyter Notebook was beyond the scope of what I expected to learn at General Assembly’s data science immersive these past couple of months. As we finished the fascinating lessons for the week, I recounted a disturbing art installation I saw at New York’s Whitney Museum Biennial exhibition last year, when I had never heard of the terms computer vision or convolutional neural networks.

Following the George Floyd protests, the use of tear gas by law enforcement once again came under scrutiny. Lawmakers in nine states introduced legislation to ban tear gas, and Portland, Denver, and Seattle have suspended its use to disperse crowds. Repressive governments use tear gas to suppress collective and often peaceful protests in public spaces, while our own government used it on even women and children at the Tijuana/San Diego border just a few years ago.
[Paper] Deep Video: Large-scale Video Classification With Convolutional Neural Network (Video Classification)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Action Recognition, Video Classification, Deep Learning

[Paper] Deep Video: Large-scale Video Classification With Convolutional Neural Network (Video Classification)

Sports-1M Dataset

In this story, Large-scale Video Classification with Convolutional Neural Network (Deep Video), by Google Research, and Stanford University, is presented. In this paper:

A spatio-temporal network using CNN is designed.

using CNN is designed. Different fusion strategies and different input resolution strategies are tried for fusing multiple frames together for video classification.

and are tried for fusing multiple frames together for video classification. This paper should be one of the first papers to work on video classification using CNN.

This is a paper in 2014 CVPR with over 4900 citations. (Sik-Ho Tsang @ Medium)
[Paper] VGGNet for COVID-19 Detection (Biomedical Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning, Covid 19

[Paper] VGGNet for COVID-19 Detection (Biomedical Image Classification)

Left: X-Ray, Middle: CT Scan, Right: Ultrasound

In this story, COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (VVGNet for COVID-19), is briefly presented. In this paper:

Broncho vascular thickening in the lesion, and traction bronchiectasis are visible during absorption stage, automatic diagnosis is possible.

Various classification models are tested , such as VGG16/VGG19, ResNet-50, Inception-v3, Xception, Inception-ResNet-v2, DenseNet, and NASNet-Large, for COVID-19 detection.

, such as VGG16/VGG19, ResNet-50, Inception-v3, Xception, Inception-ResNet-v2, DenseNet, and NASNet-Large, for COVID-19 detection. It is found that VGGNet has the most stable performance across different multimodal datasets including Ultrasound, X-ray and CT scan.

has the across different multimodal datasets including Ultrasound, X-ray and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classication models, providing an automated “second reading’’ to clinicians, assisting in the diagnosis and criticality assessment.

This is a paper in 2020 IEEE ACCESS where IEEE ACCESS is an open access journal with high impact factor of 3.745. (Sik-Ho Tsang @ Medium)
[Paper] Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours (Image Classification)
By: Sik-Ho Tsang
Tags: Neuralarchitecturesearch, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours (Image Classification)

In this story, “Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours” (Single-Path NAS), by Carnegie Mellon University, Microsoft, and Harbin Institute of Technology, is presented.

The NAS (Neural Architecture Search) problem remains challenging due to the combinatorically large design space, causing a significant searching time (at least 200 GPU-hours).

In this paper, Single-Path NAS is proposed, which drastically decreases the number of trainable parameters and the search cost down to few epochs.

Finally, hardware-efficient ConvNets can be searched in less than 4 hours.

This is a paper in 2019 ECML PKDD with over 70 citations. (Sik-Ho Tsang @ Medium)
[Paper] C3D: Learning Spatiotemporal Features with 3D Convolutional Networks (Video Classification & Action Recognition)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Action Recognition, Video Classification, Deep Learning

[Paper] C3D: Learning Spatiotemporal Features with 3D Convolutional Networks (Video Classification & Action Recognition)

In this story, Learning Spatiotemporal Features with 3D Convolutional Networks (C3D), by Facebook AI Research, and Dartmouth College, is briefly presented. I read this because this paper comes up when I read a VQA paper, it should be one of the first papers talking about 3D convolution in deep learning. In this paper:

A deep 3-dimensional convolutional network (3D ConvNet), namely C3D, is trained on multiple tasks and databases, such as video classification and action recognition.

This is a paper in 2015 ICCV with over 3900 citations. (Sik-Ho Tsang @ Medium)
How CNN depth changes after applying filters?
By: Anukool Chaturvedi
Tags: Machine Learning, AI, Neural Networks, Convolutional Network

In CNNs, a lot of variation in the dimensions of the matrix is seen due to applications of the various filter channels of varying dimensions. It sometimes confuses how after convolving filter to an input image changes the dimension of the depth.

To understand the intuition behind it, lets first take a basic Input image of size 16x16x3 and convolve it with one 3x3 kernel.

Convolution with 1 filter (3rd dimensions of both input and kernel are always equal)

Every channel of input image is multiplied with the same kernel and summed up to output a single a feature value.

After convolution with 1 kernel of size 3x3, output is of 14x14x1 feature map

In case, when there are more than 1 filters ( 2 in this case), feature output for each filter is stacked up.

The output dimension is now 14x14x2 ,because of 2 filters of size 3x3

Similarly, if N filters of size (3x3) are applied to input image with 0 padding and stride =1, the output dimension changes to (14x14xN). Each feature map is the output of each filter applied.
[Paper] MnasNet: Platform-Aware Neural Architecture Search for Mobile (Image Classification)
By: Sik-Ho Tsang
Tags: Neuralarchitecturesearch, Image Classification, Artificial Intelligence, Convolutional Network, Deep Learning

[Paper] MnasNet: Platform-Aware Neural Architecture Search for Mobile (Image Classification)

Accuracy vs. Latency Comparison, MnasNet models significantly outperforms other mobile models

In this paper, Platform-Aware Neural Architecture Search for Mobile (MnasNet), by Google Brain, and Google Inc., is presented. In this paper:

Model latency is also considered during neural architecture search where the model latency is measured using real-world inference latency by executing the model on mobile phones.

during neural architecture search where the model latency is measured using real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, a novel factorized hierarchical search space that encourages layer diversity is proposed.

This is a paper in 2019 CVPR with over 600 citations. (Sik-Ho Tsang @ Medium)
Evil Eye Hollywood Full Movie Download in Hindi Dubbed 2020 HD/720p
Tags: Bollywood Movies, Evil Eye, Hollywood, Earn Money Online, Movies

Evil Eye Hollywood Full Movie Download in Hindi Dubbed 2020 HD/720p Muhammadshaaan ·Dec 27, 2020

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Why you should watch: "The Morning Show"
By: Chloe'S Very Casual Reviews
Tags: Hollywood, Reviews, TV Shows, The Morning Show, Apple TV

*Review is spoiler free.

What I love about this show is that it's the "Me Too" movement and more. What many people fail to understand about the "Me Too" movement is that it's not as simple as holding the prominent and powerful men over the fire for their crimes. Of course it's easy for you when you're on your couch with your friends eating some chips and trying to write a tweet from behind a screen. I would like to think it is as simple as that but, imagine this was your husband or your son, your friend or your co-anchor that you've worked with every day for 15 years. How would you deal with that?

Your job. Your whole career. Your family. Your life. How can you truly know it's all worth risking when your story could never see the light of day, or even worse, ransacked by twitter mobs that are bored enough to dig up your old MySpace pics and turn you into the predator ? (ok, that's not really part of the show but you get my point)

The show conveys a in-depth personal plane of these scandals but also invokes audiences to realize the subtle unquestioned workplace behaviors that enable the predators to stay as long as they do.

Jeniffer Aniston, Reese Witherspoon and Steve Carell are just amazing at embodying the characters, and more importantly bringing their internal struggles and pain on screen. It's subtle, yet vivid and the range they have is just amazing. Having watched"The Office" and "Friends", it definitely blew my mind seeing them act in such a complex drama.

It's not just them though. It's Mark Duplass, it's Gugu Mbatha-Raw, I'm going to say her name again, Gugu Mbatha-Raw (you'll understand when you see) and Bel Powley, Karen Pittman, whose performance and talent should not go unnoticed.

Stream "The Morning Show" on Apple TV.

Definitely binge-able during the 7-day trial but well worth the $4.99 monthly subscription.
Download wonder women 2020
By: Nikhil Singh Bhadauriya
Tags: Wonder Woman, Download Movies, Hollywood, Download

Download wonder women 2020

Diana Prince lives quietly among mortals in the vibrant, sleek 1980s -- an era of excess driven by the pursuit of having it all. Though she’s come into her full powers click here
Pubblicato “Il misterioso caso del Benjamin Button da Torino a Hollywood. Nino Oxilia, il fratello segreto di Francis Scott Fitzgerald” di Patrizia Deabate.
By: Marco Asteggiano
Tags: Benjamin Button, Hollywood, Patrizia Deabate, Francis Scott Fitzgerald, Nino Oxilia

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LATEST HOLLYWOOD CELEBRITY NEWS NOW
By: Lara Smith Publishers
Tags: Celebrity News, Today, Gilberto Lima Hollywood, Hollywood

LATEST HOLLYWOOD CELEBRITY NEWS NOW

Hi to all of you.

My Friend’s

I come to wish to all of you Merry Christmas,

with the tears in the corner of my eyes,

due to this world disaster,

Probably take away a lot of hope to a lot of family’s in this Christmas Time.

And if we are human’s

The sadness hit all our hearts,From that one’s,

who the Sadness hit hard in the lost of the loved one’s,

due to this Virus.

Due to this war with out weapons.

We all have some one who need some of or comfort words,

In this Christmas Day.

Today I decided to write some thing Special from my Noble Heart.

To wish in this Christmas for all of us in the world,

Protection and hope for the best,

And nothing more than the best for all of us.

Follow the guidelines please,

Protect you and the family and the others,

Don’t discriminate anyone,

For nothing or anything,

Forgot color or race,

Forgot guns or bullets,

But Don’t forgot we all live in the same world 🌎

A Christmas Latter write to share for the world 🌎

To share with you alone,

To share with you on the street’s,

or to share with Family,

Share a word or a moment of you in this Christmas Day with the world 🌎.

Lara Smith Publishers

Ann Jones

By the Artist Gilberto Lima to the world.

https://tenor.com/buabr.gif
You Must Watch the Top 3 Songs of ‘Taylor Swift’ in 2020
By: Abhishek Singh Baghel
Tags: Taylor Adam Swift, Hollywood, News, Taylor Swift, Entertainment

Source-Twitter

You Must Watch the Top 3 Songs of ‘Taylor Swift’ in 2020 Taylor Swift has a very high chance of coming among the top or even topping the list and being the first female artist to do that

There has not been a Country/Pop singer-songwriter in recent history who has been as successful as Taylor Swift, and that is not an overstatement. According to the RIAA certification agency, Taylor Swift’s albums (6 of them) have sold over 43 million units worldwide, and over 150 million units in singles sold She stands at the 34th spot among the highest album selling artists in history.

However, she is just 28 years old and around 20 artists on the above list are not active anymore. So, Taylor Swift has a very high chance of coming among the top 5 or even topping the list and being the first female artist to do that.

According to Chartmasters.com, Taylor Swift has sold over 48 million album copies throughout her 8-album career as of 2020. However, this does not include streaming stats which make up for the biggest consumption of music in recent years. For example, Taylor’s eighth studio album ‘folklore’ only sold 1.3 million physical copies of the album while on Spotify, it gained over 80 million streams in a few days.on debut.

Read Full Here
Interesting facts about California
By: Camille P
Tags: California, Hollywood, Los Angeles, Actors, Facts

California is the most popular state in the U.S.A. California is synonymous with the glamour, money, power and the big movie industry. Almost 40 million people live in the state and the total area is 423 970 square kilometers. California became a state in september 1850. California’s flag shows a beer in profile walking on grass and above the beer in the left corner, It’s a red star. Under the bear it says : California Republic.

It’s hard to believe that Los Angeles isn’t the capital city. The capital city is actually Sacramento.

Sacramento has a population of around 500 000 people and is the oldest city in California. For thousands of years, Native Americans inhabited Sacramentos region. It started as a small town with a population of 10.000 and grew up to a big city in just 150 years. The city has a lot of fun things to see as a tourist. They have a lot of museums. The Crocker, a local downtown art museum, is the longest-running art museum in the western United States. Sacramento has a lot of trees, that is why the city has the nickname “City of Trees”. In fact, it is the second city with the most trees in the world, after Paris.

California is a beautiful state with a lot of nature and big modern cities such as Los Angeles, San Diego, San Jose and San Francisco. California is famous for its good weather, it’s usually warm and sunny and it doesn’t rain a lot and the winters are very mild. The beaches are stunning and you can expect crystal clear water and large waves.

The biggest and most popular city in California is Los Angeles. It is also the second largest city in the U.S.A. Los Angeles has a population of more than four million people. It is located near the water and makes it a very stunning city.

Los Angeles is a popular city thanks to the strong economic base in farming, oil, tourism, real estate and movies. Motion picture industry also made the city world-famous. Many celebrities earned their claim to fame here.

Hollywood is a very popular tourist attraction. It’s a neighborhood located in downtown Los Angeles. Hollywood is home to many famous television and video studios and record companies. It began as a small agriculture community and evolved into a diverse, successful metropolis where stars are born and dreams come true, for a lucky few.

The first film made in Hollywood was a short film in 1910 titled : In Old California.

By 1915, many major motion-picture companies had relocated to Hollywood from the East Coast.

Hollywood was an ideal place to produce movies since filmmakers couldn’t be sued. It also had warm and sunny weather and diverse terrain perfect for movie backgrounds.

Many Swedish actors have succeeded in Hollywood, such as Greta Garbo, who still today is one of the greatest actresses of all time. Just to name a few others, we have Stellan Skarsgård, Noomi Rapace and Malin Akerman.

California has a lot to offer, beautiful nature, delicious food, a lot of film history, top notch education and many famous people. And that’s why so many young people who have big dreams move to California.
the media
By: Jonah Angeles
Tags: Poem, Hollywood, Satire, Poetry On Medium, Poetry

Photo by Jonah Angeles (Instagram: @channelsurfcinema)

ii.

show me the way

(to the stars) //

I want to be like them

(rich, happy, loved,

and adored);

beautiful people

(with beautiful problems)

of all complexions

(but my own) //

(((who frequent masquerade ball

orgies and control the

weather with their witchy

sex rituals and (not-so)

subliminal messages in

pop song lyrics //

(with seemingly

infinite variations of the same

four (or five) chords

and the same

four (or five) emotions)

and high production quality

perfume commercials))) //

I want to be what I see on TV.
Celebrity World
By: Darrin Atkins
Tags: Celebrity, Hollywood

I hope one day

I can become

An actor and director



And then

Maybe I can own a movie studio

And make movies and TV shows



And then

Hopefully

We can all have a good time

Hanging out at awards shows
COSTUMES ARE POWERFUL: Radtai’s Top 10 Acting Tips
By: Radtai Lokutarapol
Tags: Film, Hollywood, Acting, Costume, Menswear

Carry Grant in North by Northwest (1959)

3) Costumes are Powerful

It’s Oscar Wild who says “you can never be overdressed or overeducated.” We see so many actors study their characters’ backgrounds, stories, psychology extensively, they care, however, less about how their characters would dress. If you are among those actors who care too little about costumes, I humbly think you have missed a very important point.

James Dean in Rebel Without a Cause (1955)

Marlon Brando in A Streetcar Named Desire (1951)

Imagine James Dean without red jacket in Rebel Without A Cause, Marlon Brando without a fit T-Shirt for his Stanley in A Streetcar Named Desire, Michael Douglas without suits in Wall Street, or more recently, Garry Oldman without proper attires made by a Savile Row tailor to look like Winston Churchill in The Darkest Hour, or Joaquin Phoenix without those colorful dresses in Joker. I wonder would these actors got an Oscar nominations and wins if they have no such an iconic costumes to support their roles?

Michael Douglas (right) in Wall Street (1987)

Garry Oldman as Winston Churchill in The Darkest Hour (2017)

Joaquin Phoenix in Joker (2019)

It goes back to the Hollywood in 50s when stars such as Fred Astaire or Carry Grant have to dress themselves to look their best on a screen. In order to achieve it, they went to Savile Row Tailor such as Anderson & Sheppard or Huntsman to have a suits made. These suits will hide their flaws and highlight their prominent points. In those suits, they walk, talk, and get about in the scene better, as a results, they act much better.

Fred Astaire (right) in Funny Face (1957)

Sadly, those good old days have passed. Nowadays, we have somebody whose title called “Costumes Designers” and “Costumes” supervise us an outfits. A lady called Edith Head won, however, 8 oscars for her works in costume designs. And I think Timothee Chalamet does it well as he always turns out beautifully, whether on a screen or any occasion. It is not only a matter of looking good on a screen, but as an actor it is also crucial that you know how to market yourself to be memorable and stand out of the crowd, and dressing well will help for sure.

Edith Head Hollywood Costume Designer who won 8 Oscars

Timothee Chalamet

At least, it is worth it to ask yourself before entering the scene. “Is your costume in the right fit and does it work well with you ?” If it is not, why don’t try changing it?
喬治克隆尼：「湯姆克魯斯並沒有反應過度！」
Tags: Hollywood, Tom Cruise, Mission Impossible 7, Pandemic, George Clooney

in Go Into The Story
Nicolas Cage Is Back
By: Darrin Atkins
Tags: Hollywood, Hollywood Movies, Actors, Movies, Nicolas Cage

It is hard to stay popular in Hollywood, especially with all of the changes brought about by the streaming services. Nicolas Cage keeps making movies, though, and that is what is awesome about him.

Lately, he seems to be picking up steam with his acting career. It looks like he has a new television show in the works about swear words. He might also be doing a TV show where he plays Joe Exotic. Lastly, and most intriguing, it looks like he is signed up for a movie titled The Unbearable Weight of Massive Talent.

I’m glad that Nicolas Cage is still keeping busy. He is an entertaining actor and always fun to watch. If I get a chance to hang out with him one day, which seems rather unlikely, I’m going to tell him to keep doing what he’s doing for as long as possible.
LATEST HOLLYWOOD CELEBRITY NEWS TODAY
By: Lara Smith Publishers
Tags: Latest News, Celebrity, Hollywood

LATEST HOLLYWOOD CELEBRITY NEWS TODAY

Vaccinations at Chicago-area hospital to resume after 4 workers experience adverse reactions.

LIBERTYVILLE, Ill. - A north suburban hospital is temporarily pausing coronavirus vaccinations after four workers reported feeling adverse reactions.

Since Thursday, four team members at Advocate Condell Medical Center in Libertyville experienced reactions shortly after receiving the Pfizer COVID-19 vaccination. Their symptoms included tingling and elevated heartrates, the hospital said in a statement.

The hospital also noted that the four team members represent fewer than 0.15% of the approximately 3,000 who have so far received vaccinations across Advocate Aurora Health.

The hospital released a statement Saturday saying vaccinations will resume on Sunday, Dec. 20.

Three of the team members are now home and doing well, the hospital said. One employee is receiving additional treatment.

The hospital said the CDC and local health departments concurred with the decision to resume vaccinations.

"We have eight other vaccination locations in Illinois and three in Wisconsin and are continuing at those sites as planned with no disruption," the hospital said, noting that "reactions are an expected side effect of vaccination.

Lara Smith Publishers.

Ann Jones
Unlike Pluto — 8 Legged Dreams (Lyrics)
By: Eric John
Tags: Hollywood, Music, Songs, Social Media, Song Lyrics

Unlike Pluto — 8 Legged Dreams (Lyrics)

🎤 Lyrics: Unlike Pluto — 8 Legged Dreams

[Chorus]

When I go to sleep at night

Eight-legged dreams arise

Cobwebs in my eyes

Never, never gonna wake up

Stuck in a web of lies

Paralyzed like a fly

Spiders don’t sleep at night

Never, never gonna wake up

Never, never gonna wake up

[Verse 1]

You were gonna say

My mind is a place

Where fears are raised

And my mind plays tricks

It turns and twists

It bends what I see

[Pre-Chorus]

And I will stay awake

And I will stay awake

Nightmares, go away

Oh-oh-oh

You will fall when you sleep

You will fall when you sleep

Close your eyes, sweet dreams

Oh-oh-oh

[Chorus]

When I go to sleep at night

Eight-legged dreams arise

Cobwebs in my eyes

Never, never gonna wake up

Stuck in a web of lies

Paralyzed like a fly

Spiders don’t sleep at night

Never, never gonna wake up

Never, never gonna wake up

[Verse 2]

You’re stressed when you wake

It’ll stay for a while, it’ll stay for a while now

Your sub-conscious fades

It’ll wait to unveil, it’ll wait to unveil now

[Pre-Chorus]

And I will stay awake

And I will stay awake

Nightmares, go away

Oh-oh-oh

You will fall when you sleep

You will fall when you sleep

Close your eyes, sweet dreams

Oh-oh-oh

[Chorus]

When I go to sleep at night

Eight-legged dreams arise

Cobwebs in my eyes

Never, never gonna wake up

Stuck in a web of lies

Paralyzed like a fly

Spiders don’t sleep at night

Never, never gonna wake up

Never, never gonna wake up
Me too
By: Teresa English
Tags: Culture, Celebrity, Hollywood, Black Women, Racism

…’m aware of how little we matter when we defiantly state the bullet points of our heartache, and so I refuse to pretend not to hear Mo’Nique when she cries out for the apology that’s not coming .