piggyback
Task adaptation framework
Adapting a single network to multiple tasks by learning to mask weights
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
182 stars
3 watching
27 forks
Language: Python
last commit: over 5 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 yerevann/warp | An approach to transfer learning for NLP tasks using adversarial reprogramming and word-level task-specific embeddings. | 83 |
 bhanml/masking | A project implementing a novel approach to noisy supervision in machine learning using masked loss correction and adaptation | 54 |
 wannabeog/mask-rcnn | A PyTorch implementation of the Mask R-CNN architecture | 993 |
 wasidennis/adaptsegnet | This project implements a deep learning-based approach to adapt semantic segmentation models from one domain to another. | 849 |
 beastbyteai/falcon | Automates machine learning model training using pre-set configurations and modular design. | 159 |
 ryuk17/machinelearning | This is a collection of machine learning algorithms implemented in Python 3.6. | 103 |
 prinsphield/adversarial_reprogramming | This project enables reprogramming of pre-trained neural networks to work on new tasks by fine-tuning them on smaller datasets. | 33 |
 claws-lab/jodie | A PyTorch implementation of a representation learning framework for dynamic temporal networks | 355 |
 cylance/introductiontomachinelearningforsecuritypros | A collection of examples and code snippets teaching machine learning concepts to security professionals through hands-on Python projects | 150 |
 ethanyanjiali/minchatgpt | This project demonstrates the effectiveness of reinforcement learning from human feedback (RLHF) in improving small language models like GPT-2. | 213 |
 baguasys/bagua | A framework for accelerating PyTorch deep learning training | 877 |
 pouyamghari/pof-mkl | An implementation of an online federated learning algorithm with multiple kernels for personalized machine learning | 0 |
 uber-research/learning-to-reweight-examples | Project implementing a method to improve deep learning model robustness by re-weighting examples with noisy labels | 269 |
 udibr/noisy_labels | This project explores how to adapt neural networks to noisy labels by introducing a mechanism that can learn to correct the errors. | 118 |
 netflix/pigpen | A Clojure-based implementation of the map-reduce paradigm | 567 |