GAN Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs One of the most interesting papers presented at CVPR in 2019 was Nvidia's Semantic Image Synthesis with Spatially-Adaptive Normalization. This features their new algorithm, GauGAN, which can effectively turn doodles
Generative Models A 2020 Guide to Synthetic Media Media synthesis is an exciting new area of research that has seen great advancements in the past few years. This field has the potential to completely revolutionize the way we
Computer Vision Building Image Datasets for Computer Vision Algorithms In my experience, data is one of the most challenging issues that we face in developing any ML/AI pipelines in the field of computer vision. There are two aspects
Generative Models Implementing CycleGAN for Age Conversion If you've been on any sort of social media this year, you've probably seen people uploading a recent picture of themselves right next to another picture of what they'll look
Interviews DeepFake Faceoff w/ Dr. Fakenstein & CtrlShiftFace Update! (December 2019) CtrlShiftFace has a new video Home Stallone produced with Paperspace: Deepfake technology is unlocking a new era of media production. As with all technologies, both positive and
Tutorial Unpaired Image to Image Translation with CycleGAN Yann LeCun, director of AI research at Facebook and Professor at NYU described Generative Adversarial Networks, GANs as the most interesting idea in Machine Learning in the last 10 years.
Deep Learning Building a State of the Art Bacterial Classifier with Paperspace Gradient and Fast.ai One of the great promises of Deep Learning is its applicability in a wide variety of complex tasks. Recent years have seen an explosion in the number of fields Deep
Tutorial Detecting and Localizing Pneumonia from Chest X-Ray Scans with PyTorch Over the years, we have seen very powerful models being built to distinguish between objects. These models keep getting better in terms of performance and latency day by day but
Deep Learning Going beyond torchvision models ResNets, DenseNets and Inception networks are undoubtedly some of the most powerful models out there for performing image classification and object recognition. These models have shown some promising results 聽in
Tutorial Generating an interactive Pix2Pix model with Gradient掳 and ml5.js This post will go through the process of training a generative image model using Gradient掳 and then porting the model to ml5.js, so you can interact with it in
Series: Data Augmentation Data Augmentation for Bounding Boxes: Rethinking Image Transforms for Object Detection How to adapt major image augmentation techniques for object detection purposes. We also cover the implementation of horizontal flip augmentation.
Series: Data Augmentation Data Augmentation for Bounding Boxes: Scaling and Translation We implement Scale and Translate augmentation techniques, and what to do if a portion of your bounding box is outside the image after the augmentation.
Computer Vision Data Augmentation for Bounding Boxes: Rotation and Shearing This is part 3 of the series where we are looking at ways to adapt image augmentation techniques to object detection tasks. In this part, we will cover how to implement how to rotate and shear images as well as bounding boxes using OpenCV's affine transformation features.
Series: Data Augmentation Data Augmentation For Bounding Boxes: Building Input Pipelines for Your Detector Previously, we have covered a variety of image augmentation techniques such as Flipping, rotation, shearing, scaling and translating. This part is about how to bring it all together and bake it into the input pipeline for your deep network.
ML5.js Creating your own style transfer mirror with Gradient掳 and ml5.js In this post, we will learn how to train a style transfer network with Paperspace's Gradient掳 and use the model in ml5.js to create an interactive style transfer mirror.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1 Tutorial on building YOLO v3 detector from scratch detailing how to create the network architecture from a configuration file, load the weights and designing input/output pipelines.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 2 Part 2 of the tutorial series on how to implement your own YOLO v3 object detector from scratch in PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 3 Part 3 of the tutorial series on how to implement a YOLO v3 object detector from scratch in PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 4 Part 4 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 5 Part 5 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
Computer Vision Facial Recognition Using Deep Learning Convolutional Neural Networks (CNN) and Feature Extraction Convolutional Neural Networks allow us to extract a wide range of features from images. Turns out, we can use this idea of feature extraction for face recognition too! That鈥檚 what we are going to explore in
Computer Vision Style Transfer Part 2: Face Swap! Face Transfer Using Deep Neural Nets Introduction In the previous article we looked at what style transfer was and how to use it to create your own art. Now, we will focus on how to extend the capabilities of style transfer beyond the art
Computer Vision Adversarial Autoencoders (with Pytorch) "Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake [base], supervised learning would be the icing on the
