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
183 stars
3 watching
27 forks
Language: Python
last commit: almost 6 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| An approach to transfer learning for NLP tasks using adversarial reprogramming and word-level task-specific embeddings. | 83 |
| An implementation of Masking, a method to improve the robustness of deep neural networks under noisy supervision | 54 |
| A PyTorch implementation of the Mask R-CNN architecture | 995 |
| This project implements a deep learning-based approach to adapt semantic segmentation models from one domain to another. | 851 |
| Automates machine learning model training using pre-set configurations and modular design. | 159 |
| This is a collection of machine learning algorithms implemented in Python 3.6. | 103 |
| This project enables reprogramming of pre-trained neural networks to work on new tasks by fine-tuning them on smaller datasets. | 33 |
| A PyTorch implementation of a representation learning framework for dynamic temporal networks | 362 |
| A collection of examples and code snippets teaching machine learning concepts to security professionals through hands-on Python projects | 151 |
| This project demonstrates the effectiveness of reinforcement learning from human feedback (RLHF) in improving small language models like GPT-2. | 214 |
| A framework for accelerating PyTorch deep learning training | 876 |
| An implementation of an online federated learning algorithm with multiple kernels for personalized machine learning | 0 |
| Project implementing a method to improve deep learning model robustness by re-weighting examples with noisy labels | 269 |
| This project explores how to adapt neural networks to noisy labels by introducing a mechanism that can learn to correct the errors. | 118 |
| A map-reduce framework for Clojure that compiles to Apache Pig or Cascading without requiring prior knowledge of those systems. | 567 |