Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Implementation of the Convolutional Conditional Neural Process
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (SSRN Electronic Journal)
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)
Adaptive Conditional Quantile Neural Processes - PyTorch
Implementation of a generic wrapping algorithm that can enable any meta-learning algorithm to perform well on unsegmented time-series data.
PyTorch models for AV1 artifact removal. Features a universal CRF-Preset conditional model (FiLM-based U-Net) and ultra-light Nano models (U-Net, ResNet, ...) for fast real-time video restoration (different model for each CRF bucket).
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