SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

Pdf: https://openreview.net/forum?id=v5gjXpmR8J

Code for our ICLR 2021 paper on outlier detection, titled SSD, without requiring class labels of in-distribution training data. We leverage recent advances in self-supervised representation learning followed by the cluster-based outlier detection to achieve competitive performance. This repository support both self-supervised training of networks and outlier detection evaluation of pre-trained networks. It also includes code for the two proposed extensions in the paper, i.e., 1) Few-shot outlier detection and 2) Extending SSD by including class labels, when available.

Getting started

Let's start by installing all dependencies.

pip install -r requirement.txt

Outlier detection with a pre-trained classifier

This is how we can evaluate the performance of a pre-trained ResNet50 classifier trained using SimCLR on the CIFAR-10 dataset.

CUDA_VISIBLE_DEVICES=$gpus_ids python -u eval_ssd.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --ckpt checkpoint_path --normalize --exp-name name_of_this_experiment

• training-mode: Choose from ("SimCLR", "SupCon", "SupCE"). This will choose the right network modules for the checkpoint.
• arch: Choose from available architectures in models.py
• dataset: Choose from ("cifar10", "cifar100", "svhn", "stl")
• --normalize: If set, it will normalize input images. Use only if inputs were normalized in training too.
• --exp-name: Experiment name. We will log results into a text file of this name.

The steps to evaluate with $SSD_k$ are exactly the same, except that now you have to also provide values for k and copies . k refers to how many outliers are available from each class of targeted OOD datasets while copies refers to the number of transformed instances created per available outlier image.

CUDA_VISIBLE_DEVICES=$gpu_id python -u eval_ssdk.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --ckpt checkpoint_path --normalize --k 5 --copies 10

Training a classifier using self-supervised/supervised learning

We also support training a classifier using self-supervised, supervised or a combination of both training methods. Here is an example script to train a ResNet50 network on the CIFAR-10 dataset using SimCLR.

CUDA_VISIBLE_DEVICES=$gpus_ids python -u train.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --results-dir directory_to_save_checkpoint --exp-name name_of_this_experiment --warmup --normalize

• --training-mode: Choose from ("SimCLR", "SupCon", "SupCE"). This will choose appropriate network modules, loss functions, and trainers.
• --warmup: We recommend using warmup when batch-size is large, which is often the case for self-supervised methods.

Choices for other arguments are similar to what we mentioned earlier in the evaluation section.

Reference

If you find this work helpful, consider citing it.

@inproceedings{sehwag2021ssd,
  title={SSD:  A Unified Framework for Self-Supervised Outlier Detection},
  author={Vikash Sehwag and Mung Chiang and Prateek Mittal},
 booktitle={International Conference on Learning Representations},
 year={2021},
 url={https://openreview.net/forum?id=v5gjXpmR8J}
}