Active-Passive-Losses
Loss function framework
A PyTorch-based framework for implementing normalized loss functions to improve deep learning model robustness against noisy labels.
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
134 stars
4 watching
28 forks
Language: Python
last commit: 8 months ago deep-learningdeep-neural-networksicmlicml-2020label-noisenoisy-datanoisy-labelspytorchrobust-learningunreliable-labels
Related projects:
Repository | Description | Stars |
---|---|---|
| Provides a comprehensive set of implementation of various loss functions and operators for deep learning models | 2,196 |
| An implementation of a loss function designed to improve the training of deep neural networks with noisy labels | 126 |
| Provides a framework for implementing robust loss functions to mitigate the effects of label noise in deep neural networks. | 90 |
| Provides Fenchel-Young losses for probabilistic classification in PyTorch/TensorFlow/scikit-learn. | 183 |
| A deep learning framework implementing Deeplab-resnet-101 with binary Jaccard loss surrogate, based on the Lovász hinge loss. | 97 |
| A comprehensive framework for deep reinforcement learning using PyTorch. | 1,256 |
| An implementation of a regularization technique to improve the accuracy of deep learning models trained with noisy labels. | 46 |
| An open-source reinforcement learning library for PyTorch, providing a simple and clear implementation of various algorithms. | 402 |
| An implementation of reinforcement learning algorithms for continuous control tasks using deep neural networks. | 307 |
| A PyTorch implementation of a loss function used in semantic image segmentation | 175 |
| A Python library for building and training neural networks using GPU acceleration | 1,169 |
| Develops a robust learning framework to handle noisy labels in multimodal data and improve cross-modal retrieval. | 13 |
| A Python implementation of a deep reinforcement learning algorithm combining multiple techniques for improved performance in Atari games | 1,591 |
| An optimized machine learning framework for improved performance and reduced memory usage on various hardware configurations. | 1,871 |
| Provides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels | 35 |