Computer vision

The Art of Noise

In my last several articles I talked about generative deep learning algorithms, which mostly are related to text generation tasks. So, I believe it will be interesting to change to generative algorithms for image...

The Art of Hybrid Architectures

In my previous article, I discussed how morphological feature extractors mimic the best way biological experts visually assess images. time, I need to go a step further and explore a brand new query:Can different...

From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities

Introduction: Can AI really distinguish dog breeds like human experts? Sooner or later while taking a walk, I saw a fluffy white puppy and wondered, Regardless of how closely I looked, they seemed almost...

Custom Training Pipeline for Object Detection Models

What if you desire to write the entire object detection training pipeline from scratch, so you possibly can understand each step and give you the option to customize it? That’s what I got down...

On-Device Machine Learning in Spatial Computing

The landscape of computing is undergoing a profound transformation with the emergence of spatial computing platforms(VR and AR). As we step into this recent era, the intersection of virtual reality, Augmented Reality, and on-device...

Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning

Introduction Data science is undoubtedly probably the most fascinating fields today. Following significant breakthroughs in machine learning a couple of decade ago, data science has surged in popularity throughout the tech community. Every year, we witness increasingly...

A Personal Take On Computer Vision Literature Trends in 2024

I have been constantly following the pc vision (CV) and image synthesis research scene at Arxiv and elsewhere for around five years, so trends grow to be evident over time, and so they shift...

The subsequent generation of neural networks could live in hardware

Once the network has been trained, though, things get way, way cheaper. Petersen compared his logic-gate networks with a cohort of other ultra-efficient networks, akin to binary neural networks, which use simplified perceptrons that...

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