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Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
The main features of arunmallya/piggyback are: Computer Vision Research, Continual Learning Frameworks.
Projects with overlapping indexed features include: arunmallya/packnet — Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning. zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018. aimagelab/cssl — Code implementation for "Continual Semi-Supervised Learning through Contrastive Interpolation Consistency". aimagelab/lider — Official implementation of "On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning". ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns.
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns