This repository contains simplified code for the paper:
RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning
Ameya Prabhu*, Shiven Sinha*, Ponnurangam Kumaraguru, Philip Torr, Ozan Sener+, Puneet Dokania+
- Install all requirements required to run the code on a Python 3.x by:
# First, activate a new virtual environment
pip3 install -r requirements.txt [TBA]
# Get all the required datasets into the 'data/' folder
# Setup the feature files in the directory `feats/`
python get_feats.py
python get_feats_vit.py
- To run the RanDumb model, you can simply specify conditions from arguments, an example command below:
$ python main.py --dataset cifar100 --model SLDA --augment --embed --embed_mode RanDumb --embed_dim 25000
Arguments you can freely tweak given a dataset and model:
- Use RanDumb or RanPAC embedding function (
--embed_mode) - Embedding Dimension (
--embed_dim) - Use Flip Augmentation (
--augment) - Switch mahalanobis distance on/off (
--model SLDAor--model NCM) - Switch embedding on/off (
--embed)
Additional details and default hyperparameters can be found in parse_args function in src/main.py
- Script to run RanDumb experiments given in
scripts/runall.sh
Notes: Code given here is not online. Explanation below for why it does not vary from an online version.
- Computing covariance: It is an exact rank-1 update to compute empirical covariance, so the matrix should be the same. (Also the online code is very slow to run)!
- Shrinkage estimation: We want the shrinked covariance. We use OAS method to obtain shrinkage parameters, but note that those parameters are a function of the empirical covariance matrix and again can be perfectly estimated online.
-
No lambda used when inverting: Goal is to be hyperparameter optimization free (it is not nice to do in CL, can write a paper about gains from hparam optimization), we can make it hparam free by setting
$lambda=0$ when inverting the covariance matrix-- no ridge regression style stuff here. This change leads to slightly worse results than original code used for the paper.
Overall, this code reaches similar performance (within ±0.8%) but is far more hackable-- my original (ugly, ginormous) implementation was entangled with my implementations of many many traditional kernels/online methods from which I chose this particular combination of kernel and classifier.
We hope RanDumb is valueable for your project! To cite our work:
@article{prabhu2024randumb,
title={RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning},
author={Prabhu, Ameya and Sinha, Shiven and Kumaraguru, Ponnurangam and Torr, Philip HS and Sener, Ozan and Dokania, Puneet K},
journal={arXiv preprint arXiv:2402.08823},
year={2024}
}