A Python library for end-to-end learning on surfaces. It implements pre-processing functions that include geodesic algorithms, neural network layers that operate on surfaces, visualization tools and benchmarking functionalities.
GPL-3.0 License
PyTorch and TensorFlow/Keras image models with automatic weight conversions and equal API/impleme...
A collection of computer vision pre-trained models.
Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
A curated list of dedicated resources and applications
Tools to Design or Visualize Architecture of Neural Network
Advanced Deep Learning with Keras, published by Packt
Boltzmann Machines in TensorFlow with examples
Accumulated Gradients for TensorFlow 2
Interpretability Methods for tf.keras models with Tensorflow 2.x
Keras implementation of Neural Graph Fingerprints as proposed by Duvenaud et al., 2015
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds (2...
Adversarial Autoencoders with Constant-Curvature Latent Manifolds (2018, https://arxiv.org/abs/18...
Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and i...
Learning Convolutional Neural Networks with Interactive Visualization.