ML Review

JUNE 4, 2018

These new approaches generally; Feed the image into a Convolutional Neural Network (CNN) for encoding, and run this encoding into a decoder Recurrent Neural Network (RNN) to generate an output sentence. The complete set of generated words is the output sequence (or sentence) of the network.

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Viso.ai

MAY 7, 2024

However, GoogLeNet demonstrated by using the inception module that depth and width in a neural network could be increased without exploding computations. GooLeNet – source Historical Context The concept of Convolutional Neural Networks ( CNNs ) isn’t new.

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Viso.ai

JULY 17, 2024

An image can be represented by the relationships between the activations of features detected by a convolutional neural network (CNN). Fast Style Transfer (2016) While the previous model produced decent results, it was computationally expensive and slow. Johnson et al. What is Perceptual Loss?

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Viso.ai

DECEMBER 6, 2024

2016) introduced a unified framework to detect both cyclists and pedestrians from images. Autonomous Driving applying Semantic Segmentation in autonomous vehicles Semantic segmentation is now more accurate and efficient thanks to deep learning techniques that utilize neural network models.

Computer Vision 52

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Viso.ai

MARCH 11, 2024

YOLO in 2015 became the first significant model capable of object detection with a single pass of the network. The previous approaches relied on Region-based Convolutional Neural Network (RCNN) and sliding window techniques. What is YOLO?

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Viso.ai

NOVEMBER 27, 2024

Multiple machine-learning algorithms are used for object detection, one of which is convolutional neural networks (CNNs). YOLOv1 The Original Before introducing YOLO object detection, researchers used convolutional neural network (CNN) based approaches like R-CNN and Fast R-CNN.

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Mlearning.ai

JUNE 16, 2023

Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. December 10, 2016. The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. pre-training). fine-tuning). November 21, 2018.

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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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Lexalytics

APRIL 5, 2021

Around this time (early 2016), our management team realized that to maintain relevance as a company, we would need to be able to incorporate even more ML into our product. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) 90,575 trainable parameters, placing it in the small-feature regime.

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Marktechpost

JULY 24, 2023

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. The first model, sat2rad, is a U-Net-based deep learning model that estimates rainfall in the current satellite frame time step.

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Viso.ai

DECEMBER 18, 2023

YOLO’s architecture was a significant revolution in the real-time object detection space, surpassing its predecessor – the Region-based Convolutional Neural Network (R-CNN). The backbone is a pre-trained Convolutional Neural Network (CNN) that extracts low, medium, and high-level feature maps from an input image.

Computer Vision 52

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Viso.ai

FEBRUARY 27, 2024

Farhadi, signifying a step forward in the real-time object detection space, outperforming its predecessor – the Region-based Convolutional Neural Network (R-CNN). It is a single-pass algorithm having only one neural network to predict bounding boxes and class probabilities using a full image as input. Divvala, R.

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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. Stanford University and panel researchers P. Stone and R. Brooks et al. Brooks et al.

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Viso.ai

OCTOBER 15, 2024

In the field of real-time object identification, YOLOv11 architecture is an advancement over its predecessor, the Region-based Convolutional Neural Network (R-CNN). Using an entire image as input, this single-pass approach with a single neural network predicts bounding boxes and class probabilities. Redmon, 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.

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John Snow Labs

MAY 23, 2023

VQA frameworks combine two Deep Learning architectures to deliver the final answer: Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Network (RNN) (and its special variant Long Short Term Memory networks or LSTM) for NLP processing.

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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. Top Computer Vision Computer vision continues to evolve and make rapid progress.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. spaCy’s default token-vector encoding settings are a depth 4 convolutional neural network with width 96, and hash embeddings with 2000 rows.

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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. The original YOLO object detector was first released in 2016. YOLOv7 applied for computer vision in Aviation – built on Viso Suite What is YOLO in computer vision?

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Sebastian Ruder

JANUARY 19, 2021

2016 ; Webster et al.,  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. Pre-trained language models were found to be prone to generating toxic language ( Gehman et al.,

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AI Weekly

JUNE 29, 2023

In the News Next DeepMind's Algorithm To Eclipse ChatGPT IN 2016, an AI program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. Powered by pluto.fi June 15, 2023 /PRNewswire/ -- Quantum Computing Inc. ("QCi"

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ML Review

MAY 3, 2018

2016) — “ LipNet: End-to-End Sentence-level Lipreading.” [17] 22] On a high level in the architecture, the frames extracted from a video sequence are processed in small sets within a Convolutional Neural Network (CNN), [23] while an LSTM-variant runs on the CNN output sequentially to generate output characters.

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Marek Rei

JANUARY 17, 2018

The only filter that I applied was to exclude papers older than 2016, as the goal is to give an overview of the more recent work. NAACL 2016. Neural activity by brain region, from Wehbe et al. NAACL 2016. The papers are not selected or ordered based on any criteria. I set out to summarise 50 papers. Copenhagen.

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). They have the following advantages: Patch-based approach : The input image is divided into fixed-size patches, which are then linearly embedded. 2019; Lou, et al.

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