We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at the library's lowest level of abstraction, we provide building blocks for creating models and data preprocessing pipelines, and at the library's highest level of abstraction, we provide pretrained "task" models for popular architectures such as Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. Task models have built-in preprocessing, pretrained weights, and can be fine-tuned on raw inputs. To enable efficient training, we support XLA compilation for all models, and run all preprocessing via a compiled graph of TensorFlow operations using the tf.data API. The libraries are fully open-source (Apache 2.0 license) and available on GitHub. Keywords: KerasCV, KerasNLP, Keras multi-backend, Deep learning, Generative AI

Using spectral multiplication to compute convolution in neural networks has been investigated by a number of researchers because of its potential in speeding up computations for large images. However, previous methods require the learning of arbitrarily large convolution filters in the spectral domain, causing two untenable problems: an explosion in the number of trainable parameters per filter and an inability to reuse filters across images of differing sizes. To address this, we propose the usage of spectral parametric functions to represent massive spectral domain filters with only a few trainable parameters. Our empirical analysis suggests that the proposed functions maintain the benefits of arbitrarily large filters (such as improved rate of convergence in training, accuracy, and stability) while relying on significantly fewer trainable parameters.