Deep_SLDA
This is a PyTorch implementation of the Deep Streaming Linear Discriminant Analysis (SLDA) algorithm from our CVPRW-2020 paper. An arXiv pre-print of our paper is available, as well as the published paper.
Deep SLDA combines a feature extractor with LDA to perform streaming image classification and can be thought of as a way to train the output layer of a neural network. Deep SLDA only requires the storage of a single shared covariance matrix beyond its feature extraction CNN, making its memory requirements very low, e.g., 0.001 GB for our experiments with ResNet-18. Further, once initialized, Deep SLDA is able to train incrementally on the ImageNet dataset in roughly 30 minutes on a Titan X GPU. This is remarkable as methods like iCaRL require 3.011 GB of storage beyond the CNN and require 62 hours to train on the same hardware.
An additional Deep SLDA implementation directly using the CORe50 dataset and scenarios defined in the original CORe50 paper is located here
Dependences
- Tested with Python 3.6 and PyTorch 1.1.0, or Python 3.7 and PyTorch 1.3.1, NumPy, NVIDIA GPU
- Dataset:
- ImageNet-1K (ILSVRC2012) -- Download the ImageNet-1K dataset and move validation images to labeled sub-folders. See link.
Usage
To replicate the SLDA experiments on ImageNet-1K, change necessary paths and run from terminal:
slda_imagenet.sh
Alternatively, setup appropriate parameters and run directly in python:
python experiment.py
Implementation Notes
When run, the script will save out network probabilities (torch files), accuracies (json files), and the SLDA means and covariance weights (torch files) after every 100 classes in a directory called ./streaming_experiments/*expt_name*.
We have included all necessary files to replicate our ImageNet-1K experiments. Note that the checkpoint file provided in image_files has only been trained on the base 100 classes. However, for other datasets you may want a checkpoint trained on the entire ImageNet-1K dataset, e.g., our CORe50 experiments. Simply change line 196 of experiment.py to feature_extraction_model = get_feature_extraction_model(None, imagenet_pretrained=True).eval() to use ImageNet-1K pre-trained weights from PyTorch.
Other datasets can be used by implementing a PyTorch dataloader for them.
If you would like to start streaming from scratch without a base initialization phase, simply leave out the call to fit_base.
Results on ImageNet ILSVRC-2012
Citation
If using this code, please cite our paper.
@InProceedings{Hayes_2020_CVPR_Workshops,
author = {Hayes, Tyler L. and Kanan, Christopher},
title = {Lifelong Machine Learning With Deep Streaming Linear Discriminant Analysis},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
