README.md

Memorization-Curvature (ICML 2024 Spotlight)

This code is the official implementation for the paper, Memorization Through the Lens of Curvature of Loss Function Around Samples. This code contains the experiments described in the paper.

Accepted at ICML 2024 Spotlight (3.5% Acceptance), if you use this github repo consider citing our work

@inproceedings{
    garg2024memorization,
    title={Memorization Through the Lens of Curvature of Loss Function Around Samples},
    author={Garg, Isha and Ravikumar, Deepak and Roy, Kaushik},
    booktitle={Forty-first International Conference on Machine Learning},
    year={2024},
    url={https://openreview.net/forum?id=WQbDS9RydY}

}

Full paper available paper link. You may also be interested in our work (ICML '24) on Privacy Memorization and Curvature paper, code.

Setup

Create a folder ./pretrained/<dataset name> and ./pretrained/<dataset name>/temp i.e.

mkdir pretrained
mkdir pretrained/imagenet
mkdir pretrained/imagenet/temp

Update the paths for the datasets in ./utils/load_dataset.py, replace the string 'Set path'

Code Flow

1. Train model using train_<dataset>.py
   • Trains the models and saves checkpoint every epoch for analysis in step 2.
2. Run score_<dataset>_checkpoint.py
   • Computes the input loss curvature for the model for each epoch and saves in ./curv_scores
3. Use the analyze_<dataset>_curv.ipynb for analysis

Training

To train ImageNet model use train_imagenet.py. This code is made to be trained in a distributed manner and makes use of torch run. To train on 1 node and 1 GPU use the following command

OMP_NUM_THREADS=12 torchrun \
--nproc_per_node=1 \
--nnodes=1 \
--node_rank=0 \
--rdzv_id=456 \
--rdzv_backend=c10d \
--rdzv_endpoint=<ip-address-of-machine>:26903 \ 
train_imagenet.py

For multi-gpu single node use

OMP_NUM_THREADS=12 torchrun \
--nproc_per_node=3 \
--nnodes=1 \
--node_rank=0 \
--rdzv_id=456 \
--rdzv_backend=c10d \
--rdzv_endpoint=<ip-address-of-machine>:26903 \ 
train_imagenet.py

This uses 3 GPUs on a single node Output of each node is logged into ./logs folder with a corresponding log file.

Visualizations

See visualizations for additional results and higher quality images from the paper.