Authoring

Parametric Spectral Filters for Fast Converging, Scalable Convolutional Neural Networks

Luke Wood, Eric C. Larson

Primary author of 2021 ICASSP Publication: Parametric Spectral Filters for Fast Converging, Scalable Convolutional Neural Networks. Please see GitHub repo for full information.

Conference Presentation: Latent Diffusion Models

Conference presentation at Devoxx, This talk walks through the architecture and theory enabling these models to generate novel yet coherent images, explores some more advanced uses of text to image models, and lastly shows you how to get started generating images using KerasCV, the most optimized implementation of StableDiffusion available to date.

Teach StableDiffusion new concepts via Textual Inversion

Learning new visual concepts with KerasCV's StableDiffusion implementation.

High-performance image generation using Stable Diffusion in KerasCV

Francois Chollet, Luke Wood, Divam Gupta

Generate new images using KerasCV's StableDiffusion model.

A walk through latent space with Stable Diffusion

Ian Stenbit, Francois Chollet, Luke Wood

Explore the latent manifold of Stable Diffusion.

The Definitive Guide to Object Detection

Luke Wood

My "definitive guide" to object detection is live on keras.io! This tutorial is a bit more like a textbook chapter than a typical keras.io tutorial, but by the end of it you will have an extremely strong sense of how to tackle object detection problems with deep learning.

Custom Image Augmentations with BaseImageAugmentationLayer

Luke Wood

Use BaseImageAugmentationLayer to implement custom data augmentations.

CutMix, MixUp, and RandAugment image augmentation with KerasCV

Luke Wood

Use KerasCV to augment images with CutMix, MixUp, RandAugment, and more.

Evaluating and exporting scikit-learn metrics in a Keras callback

Luke Wood

This example shows how to use Keras callbacks to evaluate and export non-TensorFlow based metrics.

Customizing the convolution operation of a Conv2D layer

Luke Wood

This example shows how to implement custom convolution layers using the Conv.convolution_op() API.

Writing Keras Models With TensorFlow NumPy

Luke Wood

Overview of how to use the TensorFlow NumPy API to write Keras models. Published on keras.io

Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation

Pre-print written alongside Francois Chollet on a novel algorithm to closely approximate Mean Average Precision within the constraints of the TensorFlow graph. The algorithm used in the publication is used in the KerasCV COCO metric implementation, and can be used to perform train time evaluation with any KerasCV object detection model.

Deep Learning Object Detection Approaches to Signal Identification

Pre-print written for ICASSP 2022. We decided not to continue this line of work, but our results and our spectrogram object detection dataset are open source and available on GitHub. If you'd like to finish this work and attempt to get it published, feel free to reach out.