Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the effect of crowding in artificia...
Deep learning-based medical image classification techniques are rapidly advancing in medical image analysis, making it crucial to develop accurate and trustworthy models that can be efficiently deployed across diverse clinical scenarios. Concept Bott...
Progress. Our foundation since day one. On the road, in the home, and in business Whether it's on the road, in the home, or in business, at the heart of Progressive is a deep-rooted dedication to helping …
First-break picking is a pivotal procedure in processing microseismic data for geophysics and resource exploration. Recent advancements in deep learning have catalyzed the evolution of automated methods for identifying first-break. Nevertheless, the...
Populate deep nested hashes without initialization situps (⭐ 3)
Deep learning library featuring a higher-level API for TensorFlow. (⭐ 9601)
The Regulatory & Compliance Deep Agent is a single-tenant, cloud-native ideation application designed to assist planners, COOs, compliance officers, and auditors in drafting planning documents with continuous, domain-informed suggestions and compliance guidanc…
A neural processing unit (NPU), also known as an AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer
Style transfer, deep learning, feature transform (⭐ 11176)
Interstellar transport is an object of interest in many sci-fi stories. In history a lot of sci-fi predictions have turned into reality, such as communications satellites, deep-sea submarines and journies to the moon. In this work we study some physi...
The Deep neural networks (DNNs) have achieved great success on a variety of computer vision tasks, however, they are highly vulnerable to adversarial attacks. To address this problem, we propose to improve the local smoothness of the representation s...
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions. Deep learning models have thrived in the non-sequential learning paradigm; ho...
Integrating visible and infrared images into one high-quality image, also known as visible and infrared image fusion, is a challenging yet critical task for many downstream vision tasks. Most existing works utilize pretrained deep neural networks or...
We have investigated the spatial extent and structure of the Draco dwarf spheroidal galaxy using deep wide-field multicolor CCD photometry from the Sloan Digital Sky Survey (SDSS). Our study covers an area of 27 square degrees around the center of...
Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)…
Hi folks, First, just a quick note of appreciation: the response to last night’s deep dive on the short report was honestly overwhelming. There were hundreds of thoughtful comments, DMs, and a bunc...
This repository contains human electrocardiogram data (ECG) data used in Wavelet Toolbox machine and deep learning examples (⭐ 60)
A list of papers for physiological signal classification using machine learning/deep learning. (⭐ 246)
A deep learning framework for physiological data processing and understanding. (⭐ 109)
Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using classic machine learning, rather than recent deep learning techniques. We p...