Artificial Corner

JUNE 23, 2023

Like U-Net, TransUnet includes two main sections: Encoder (Vision Transformer): Vision Transformer encodes patches of feature maps that are generated by a convolution neural network. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020) [3] Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, E.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Six algorithms available in Forecast were tested: Convolutional Neural Network – Quantile Regression (CNN-QR), DeepAR+ , Prophet , Non-Parametric Time Series (NPTS), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS).

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Heartbeat

DECEMBER 8, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. years, a 0.16% increase from 2021.

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Flipboard

MAY 17, 2023

CEOs of major auto companies were all saying by 2020 or 2021 or 2022, roughly. Convolutional neural networks being able to label regions of an image. So there’s a case where AI, convolutional neural networks—which, by the way, applied to vision is 10 years old—is going to make a difference.

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Mlearning.ai

APRIL 8, 2023

With the rise of deep learning (deep learning means multiple levels of neural networks) and neural networks, models such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) began to be used in NLP. 2018) “ Language models are few-shot learners ” by Brown et al.

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Viso.ai

SEPTEMBER 2, 2024

According to the data from the recruiting platforms – job listings that look for artificial intelligence or computer vision specialists doubled from 2021 to 2023. As many areas of artificial intelligence (AI) have experienced exponential growth, computer vision is no exception. It’s definitely an exciting time to be in AI.

Computer Vision 52

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Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). The process of locating and identifying objects of interest in an image or video is part of the object detection method, a popular computer vision technique.

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Google Research AI blog

FEBRUARY 23, 2023

In 2021, we shared some of our early work using smartphone cameras to measure heart rate and to help identify skin conditions. Previous work had shown that convolutional neural networks (CNNs) could interpret ultrasounds acquired by trained sonographers using a standardized acquisition protocol.

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Viso.ai

APRIL 25, 2024

Today’s boom in CV started with the implementation of deep learning models and convolutional neural networks (CNN). 2021) published their research Anomaly Detection in E-Health Applications Using Lightweight CNN Architecture. Lightweight computer vision models allow the users to deploy them on mobile and edge devices.

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Unite.AI

OCTOBER 10, 2024

Source: [link] ‘This phenomenon,' they explain, ‘termed “compression helps” in the [2021] paper, is justified by the fact that compression can remove noise and disturbing background features, thereby highlighting the main object in an image, which helps DNNs make better prediction.'

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PyImageSearch

SEPTEMBER 23, 2024

in 2021 in their paper titled “ An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale.” The strength of Vision Transformers lies in their ability to model relationships between distant parts of an image, which traditional convolutional neural networks (CNNs) might struggle to capture.

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Viso.ai

DECEMBER 10, 2023

In comparison, the YOLOR algorithm released in 2021 achieves inference times of 12ms on the same benchmark, surpassing the popular YOLOv4 and YOLOv3 deep learning algorithms. Training of Neural Networks for Image Recognition The images from the created dataset are fed into a neural network algorithm.

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Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

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NVIDIA

OCTOBER 4, 2023

Harnessing the raw power of NVIDIA GPUs and aided by a network of thousands of cameras dotting the Californian landscape, DigitalPath has refined a convolutional neural network to spot signs of fire in real time. And the total dollar damage of wildfires in California from 2019 to 2021 was estimated at over $25 billion.

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AWS Machine Learning Blog

MAY 15, 2023

We ultimately selected the Amazon CNN-QR (Convolutional Neural Network – Quantile Regression) algorithm for our forecasting due to its high performance in the backtest process. He worked at Turkcell, mainly focused on time series forecasting, data visualization, and network automation.

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Marktechpost

JULY 24, 2023

The EC’s 10-day wind-field forecasts were corrected for rolling bias in real-time from December 2020 to November 2021 throughout all four seasons. This model predicts rainfall for the full satellite area using convolutional neural networks’ spatial invariance, even if radar data is only available for a smaller area.

Deep Learning 52

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Yes 2021 – today ViT Variants There are several ViT variants, including DeiT, PVT, TNT, Swin, and CSWin (2022).

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Google Research AI blog

FEBRUARY 3, 2023

Real-time boundary tracking of the 2021-2022 Wrattonbully bushfire , shown as a red polygon in Google Maps. All inputs are resampled to a uniform 1 km–square grid and fed into a convolutional neural network (CNN). Inputs Wildfire boundary tracking requires balancing spatial resolution and update frequency.

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Mlearning.ai

NOVEMBER 29, 2023

Topical Methods of Time Series Forecasting Convolutional Neural Network (CNN) Although analysis of image datasets is considered their main field of application, convolutional neural networks can show even better results than RNNs in time series prediction cases involving other types of spatial data.

Machine Learning 52

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Viso.ai

JUNE 4, 2024

Many of these methods utilize deep learning and Convolutional Neural Networks (CNNs) to create point cloud processing. Deep Learning Convolutional-Based Method Processing irregular, unstructured point cloud data remains a formidable challenge, despite the deep learning’s success in processing structured 2D image data.

Computer Vision 52

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Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Moley Robotic Kitchen In 2021, we witnessed the development of Moley, a robotic chef with two smoothly moving arms and a stack of pre-programmed recipes. Brooks et al.

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DataRobot Blog

MARCH 9, 2022

The company was acquired by DataRobot in 2021. The idea is that instead of performing convolutions on image pixels, the model can instead perform those convolutions in the embedded feature space of the words in a sentence. This article was originally published at Algorithimia’s website. Find out more about DataRobot MLOps here.

Machine Learning 52

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Viso.ai

MAY 20, 2023

Most algorithms use a convolutional neural network (CNN) to extract features from the image to predict the probability of learned classes. YOLOv7 applied for computer vision in Aviation – built on Viso Suite What is YOLO in computer vision? The best-performing object detectors are: YOLOv3 model, introduced by Redmon et al.

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Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

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Viso.ai

NOVEMBER 27, 2023

R-CNN (Region-based Convolutional Neural Networks) is one of the most commonly used machine learning models today. Object Detection Applied in Outer Space The high-resolution, high-sensitivity instruments carried by the James Webb Space Telescope, were launched in 2021.

Computer Vision 52

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Viso.ai

MAY 14, 2024

Convolutional Neural Network Zhang et al. 2021) created a CNN-based fusion framework to extract features and reconstruct images by using a carefully designed loss function. It splits the original image into regions so that every region is in focus for at least one channel of the image. How to Implement Image Fusion?

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. He started at the NFL in February 2020 as a Data Scientist and was promoted to his current role in December 2021. and Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ).

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deepsense.ai

MAY 7, 2023

This solution is based on several Convolutional Neural Networks that work in a cascade fashion to locate the face with some landmarks in an image. The first network is called a Proposal Network – it parses the image and selects several bounding boxes that surround an object of interest: a face, in our case.

Neural Network 52

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Sebastian Ruder

JANUARY 19, 2021

 While Transformers have achieved large success in NLP, they were—up until recently—less successful in computer vision where convolutional neural networks (CNNs) still reigned supreme. 2020 ) employed a CNN to compute image features, later models were completely convolution-free.

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Google Research AI blog

JANUARY 18, 2023

One trend that started with our work on Vision Transformers in 2020 is to use the Transformer architecture in computer vision models rather than convolutional neural networks. Similar updates were published in 2021 , 2020 , and 2019. Top Computer Vision Computer vision continues to evolve and make rapid progress.

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Unite.AI

JUNE 25, 2024

Through this adversarial process, both networks improve, leading to the creation of highly realistic synthetic media. Recent advancements in machine learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) , have further enhanced the realism of deepfakes.

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