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Group-equivariant neural networks with escnn

Group-equivariant neural networks with escnn

Group-equivariant neural networks with escnn

Group-equivariant neural networks with escnn

Escnn, built on PyTorch, is a library that, in the spirit of Geometric Deep Learning, provides a high-level interface to designing and training group-equivariant neural networks. This post introduces important mathematical concepts, the library’s key actors, and essential library use

Machine Learning Best Practices Ai Torch Concepts Image Recognition Image Processing

De-noising Diffusion with torch

De-noising Diffusion with torch

De-noising Diffusion with torch

De-noising Diffusion with torch

Currently, in generative deep learning, no other approach seems to outperform the family of diffusion models. Would you like to try for yourself? If so, our torch implementation of de-noising diffusion provides an easy-to-use, easy-to-configure interface

Daniel Falbel, Sigrid Keydana

Machine Learning Ai Torch Image Recognition Image Processing Generative Models

Implementing rotation equivariance: Group-equivariant CNN from scratch

Implementing rotation equivariance: Group-equivariant CNN from scratch

Implementing rotation equivariance: Group-equivariant CNN from scratch

Implementing rotation equivariance: Group-equivariant CNN from scratch

We code up a simple group-equivariant convolutional neural network (GCNN) that is equivariant to rotation. The world may be upside down, but the network will know

Machine Learning Ai Torch Spatial Data Image Recognition Image Processing

Train in R, run on Android: Image segmentation with torch

Train in R, run on Android: Image segmentation with torch

Train in R, run on Android: Image segmentation with torch

Train in R, run on Android: Image segmentation with torch

We train a model for image segmentation in R, using torch together with luz, its high-level interface. We then JIT-trace the model on example input, so as to obtain an optimized representation that can run with no R installed. Finally, we show the model being run on Android

Machine Learning Ai Torch Image Recognition Image Processing

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we’d like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we’d have at our disposal an architecture that is both recurrent and convolutional. In this post, we build a convolutional LSTM with torch

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

Brain image segmentation with torch

Brain image segmentation with torch

Brain image segmentation with torch

Brain image segmentation with torch

The need to segment images arises in various sciences and their applications, many of which are vital to human (and animal) life. In this introductory post, we train a U-Net to mark lesioned regions on MRI brain scans

Machine Learning Ai Torch Image Recognition Image Processing

Classifying images with torch

Classifying images with torch

Classifying images with torch

Classifying images with torch

We learn about transfer learning, input pipelines, and learning rate schedulers, all while using torch to tell apart species of beautiful birds

Machine Learning Ai Torch Image Recognition Image Processing

Deepfake detection challenge from R

Deepfake detection challenge from R

Deepfake detection challenge from R

Deepfake detection challenge from R

A couple of months ago, Amazon, Facebook, Microsoft, and other contributors initiated a challenge consisting of telling apart real and AI-generated (“fake”) videos. We show how to approach this challenge from R

Turgut Abdullayev

reticulate Machine Learning Ai Image Recognition Image Processing

Easy PixelCNN with tfprobability

Easy PixelCNN with tfprobability

Easy PixelCNN with tfprobability

Easy PixelCNN with tfprobability

PixelCNN is a deep learning architecture - or bundle of architectures - designed to generate highly realistic-looking images. To use it, no reverse-engineering of arXiv papers or search for reference implementations is required: TensorFlow Probability and its R wrapper, tfprobability, now include a PixelCNN distribution that can be used to train a straightforwardly-defined neural network in a parameterizable way

keras3 Machine Learning Ai Image Recognition Image Processing TensorFlow Keras Probabilistic Machine Learning Probabilistic Deep Learning Generative Models

Image segmentation with U-Net

Image segmentation with U-Net

Image segmentation with U-Net

Image segmentation with U-Net

In image segmentation, every pixel of an image is assigned a class. Depending on the application, classes could be different cell types; or the task could be binary, as in “cancer cell yes or no?”. Area of application notwithstanding, the established neural network architecture of choice is U-Net. In this post, we show how to preprocess data and train a U-Net model on the Kaggle Carvana image segmentation data

Daniel Falbel, Sigrid Keydana

keras3 Machine Learning Ai Image Recognition Image Processing TensorFlow Keras

Concepts in object detection

Concepts in object detection

Concepts in object detection

Concepts in object detection

As shown in a previous post, naming and locating a single object in an image is a task that may be approached in a straightforward way. This is not the same with general object detection, though - naming and locating several objects at once, with no prior information about how many objects are supposed to be detected. In this post, we explain the steps involved in coding a basic single-shot object detector: Not unlike SSD (Single-shot Multibox Detector), but simplified and designed not for best performance, but comprehensibility

keras3 Machine Learning Ai TensorFlow Keras Image Recognition Image Processing

You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks

You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks

You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks

You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks

In deep learning, there is no obvious way of obtaining uncertainty estimates. In 2016, Gal and Ghahramani proposed a method that is both theoretically grounded and practical: use dropout at test time. In this post, we introduce a refined version of this method (Gal et al. 2017) that has the network itself learn how uncertain it is

keras3 Machine Learning Ai Image Recognition Image Processing Probabilistic Machine Learning Probabilistic Deep Learning TensorFlow Keras

Naming and locating objects in images

Naming and locating objects in images

Naming and locating objects in images

Naming and locating objects in images

Object detection (the act of classifying and localizing multiple objects in a scene) is one of the more difficult, but very relevant in practice deep learning tasks. We’ll build up to it in several posts. Here we start with the simpler tasks of naming and locating a single object

keras3 Machine Learning Ai TensorFlow Keras Image Recognition Image Processing

Representation learning with MMD-VAE

Representation learning with MMD-VAE

Representation learning with MMD-VAE

Representation learning with MMD-VAE

Like GANs, variational autoencoders (VAEs) are often used to generate images. However, VAEs add an additional promise: namely, to model an underlying latent space. Here, we first look at a typical implementation that maximizes the evidence lower bound. Then, we compare it to one of the more recent competitors, MMD-VAE, from the Info-VAE (information maximizing VAE) family

keras3 Machine Learning Ai TensorFlow Keras Unsupervised Learning Image Recognition Image Processing

Image-to-image translation with pix2pix

Image-to-image translation with pix2pix

Image-to-image translation with pix2pix

Image-to-image translation with pix2pix

Conditional GANs (cGANs) may be used to generate one type of object based on another - e.g., a map based on a photo, or a color video based on black-and-white. Here, we show how to implement the pix2pix approach with Keras and eager execution

keras3 Machine Learning Ai TensorFlow Keras Image Recognition Image Processing Generative Models

Attention-based Image Captioning with Keras

Attention-based Image Captioning with Keras

Attention-based Image Captioning with Keras

Attention-based Image Captioning with Keras

Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation

keras3 Machine Learning Ai Natural Language Processing TensorFlow Keras Image Recognition Image Processing

Neural style transfer with eager execution and Keras

Neural style transfer with eager execution and Keras

Neural style transfer with eager execution and Keras

Neural style transfer with eager execution and Keras

Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily perform style transfer on your own images

keras3 Machine Learning Ai TensorFlow Keras Generative Models Image Recognition Image Processing

Generating images with Keras and TensorFlow eager execution

Generating images with Keras and TensorFlow eager execution

Generating images with Keras and TensorFlow eager execution

Generating images with Keras and TensorFlow eager execution

Generative adversarial networks (GANs) are a popular deep learning approach to generating new entities (often but not always images). We show how to code them using Keras and TensorFlow eager execution

keras3 Machine Learning Ai TensorFlow Keras Generative Models Image Recognition Image Processing

lime v0.4: The Kitten Picture Edition

lime v0.4: The Kitten Picture Edition

lime v0.4: The Kitten Picture Edition

lime v0.4: The Kitten Picture Edition

A new major release of lime has landed on CRAN. lime is an R port of the Python library of the same name by Marco Ribeiro that allows the user to pry open black box machine learning models and explain their outcomes on a per-observation basis

Thomas Lin Pedersen

keras3 Machine Learning Ai Packages Releases TensorFlow Keras Explainability Image Recognition Image Processing

Image Classification on Small Datasets with Keras

Image Classification on Small Datasets with Keras

Image Classification on Small Datasets with Keras

Image Classification on Small Datasets with Keras

Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine tuning from a pretrained network

François Chollet, J.J. Allaire

keras3 Machine Learning Ai TensorFlow Keras Image Recognition Image Processing

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