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# CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
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# Masterarbeit: Anomalie-Detektion in Zellbildern zur Anwendung der Leukaemieerkennung
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Official PyTorch implementation of
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["**CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances**"](
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https://arxiv.org/abs/2007.08176) (NeurIPS 2020) by
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[Jihoon Tack*](https://jihoontack.github.io),
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[Sangwoo Mo*](https://sites.google.com/view/sangwoomo),
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[Jongheon Jeong](https://sites.google.com/view/jongheonj),
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and [Jinwoo Shin](http://alinlab.kaist.ac.kr/shin.html).
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CSI Methode mit Codeanpassungen bzw. Ergaenzungen.
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<p align="center">
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<img src=figures/shifting_transformations.png width="900">
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</p>
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**Offizieller Release von Tack et al.:
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https://github.com/alinlab/CSI#environments**
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## 1. Requirements
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### Environments
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Currently, requires following packages
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- python 3.6+
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- torch 1.4+
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- torchvision 0.5+
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- CUDA 10.1+
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- scikit-learn 0.22+
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- tensorboard 2.0+
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- [torchlars](https://github.com/kakaobrain/torchlars) == 0.1.2
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- [pytorch-gradual-warmup-lr](https://github.com/ildoonet/pytorch-gradual-warmup-lr) packages
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- [apex](https://github.com/NVIDIA/apex) == 0.1
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- [diffdist](https://github.com/ag14774/diffdist) == 0.1
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### Datasets
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For CIFAR, please download the following datasets to `~/data`.
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* [LSUN_resize](https://www.dropbox.com/s/moqh2wh8696c3yl/LSUN_resize.tar.gz),
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[ImageNet_resize](https://www.dropbox.com/s/kp3my3412u5k9rl/Imagenet_resize.tar.gz)
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* [LSUN_fix](https://drive.google.com/file/d/1KVWj9xpHfVwGcErH5huVujk9snhEGOxE/view?usp=sharing),
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[ImageNet_fix](https://drive.google.com/file/d/1sO_-noq10mmziB1ECDyNhD5T4u5otyKA/view?usp=sharing)
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For ImageNet-30, please download the following datasets to `~/data`.
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* [ImageNet-30-train](https://drive.google.com/file/d/1B5c39Fc3haOPzlehzmpTLz6xLtGyKEy4/view),
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[ImageNet-30-test](https://drive.google.com/file/d/13xzVuQMEhSnBRZr-YaaO08coLU2dxAUq/view)
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* [CUB-200](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html),
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[Stanford Dogs](http://vision.stanford.edu/aditya86/ImageNetDogs/),
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[Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/),
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[Oxford flowers](https://www.robots.ox.ac.uk/~vgg/data/flowers/),
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[Food-101](https://www.kaggle.com/dansbecker/food-101),
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[Places-365](http://data.csail.mit.edu/places/places365/val_256.tar),
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[Caltech-256](https://www.kaggle.com/jessicali9530/caltech256),
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[DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/)
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For Food-101, remove hotdog class to avoid overlap.
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## 2. Training
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Currently, all code examples are assuming distributed launch with 4 multi GPUs.
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To run the code with single GPU, remove `-m torch.distributed.launch --nproc_per_node=4`.
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### Unlabeled one-class & multi-class
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To train unlabeled one-class & multi-class models in the paper, run this command:
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```train
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CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --dataset <DATASET> --model <NETWORK> --mode simclr_CSI --shift_trans_type rotation --batch_size 32 --one_class_idx <One-Class-Index>
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```
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> Option --one_class_idx denotes the in-distribution of one-class training.
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> For multi-class training, set --one_class_idx as None.
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> To run SimCLR simply change --mode to simclr.
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> Total batch size should be 512 = 4 (GPU) * 32 (--batch_size option) * 4 (cardinality of shifted transformation set).
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### Labeled multi-class
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To train labeled multi-class model (confidence calibrated classifier) in the paper, run this command:
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```train
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# Representation train
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CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --dataset <DATASET> --model <NETWORK> --mode sup_simclr_CSI --shift_trans_type rotation --batch_size 32 --epoch 700
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# Linear layer train
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python train.py --mode sup_CSI_linear --dataset <DATASET> --model <NETWORK> --batch_size 32 --epoch 100 --shift_trans_type rotation --load_path <MODEL_PATH>
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```
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> To run SupCLR simply change --mode to sup_simclr, sup_linear for representation training and linear layer training respectively.
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> Total batch size should be same as above. Currently only supports rotation for shifted transformation.
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## 3. Evaluation
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We provide the checkpoint of the CSI pre-trained model. Download the checkpoint from the following link:
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- One-class CIFAR-10: [ResNet-18](https://drive.google.com/drive/folders/1z02i0G_lzrZe0NwpH-tnjpO8pYHV7mE9?usp=sharing)
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- Unlabeled (multi-class) CIFAR-10: [ResNet-18](https://drive.google.com/file/d/1yUq6Si6hWaMa1uYyLDHk0A4BrPIa8ECV/view?usp=sharing)
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- Unlabeled (multi-class) ImageNet-30: [ResNet-18](https://drive.google.com/file/d/1KucQWSik8RyoJgU-fz8XLmCWhvMOP7fT/view?usp=sharing)
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- Labeled (multi-class) CIFAR-10: [ResNet-18](https://drive.google.com/file/d/1rW2-0MJEzPHLb_PAW-LvCivHt-TkDpRO/view?usp=sharing)
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### Unlabeled one-class & multi-class
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To evaluate my model on unlabeled one-class & multi-class out-of-distribution (OOD) detection setting, run this command:
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```eval
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python eval.py --mode ood_pre --dataset <DATASET> --model <NETWORK> --ood_score CSI --shift_trans_type rotation --print_score --ood_samples 10 --resize_factor 0.54 --resize_fix --one_class_idx <One-Class-Index> --load_path <MODEL_PATH>
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```
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> Option --one_class_idx denotes the in-distribution of one-class evaluation.
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> For multi-class evaluation, set --one_class_idx as None.
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> The resize_factor & resize fix option fix the cropping size of RandomResizedCrop().
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> For SimCLR evaluation, change --ood_score to simclr.
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### Labeled multi-class
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To evaluate my model on labeled multi-class accuracy, ECE, OOD detection setting, run this command:
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```eval
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# OOD AUROC
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python eval.py --mode ood --ood_score baseline_marginalized --print_score --dataset <DATASET> --model <NETWORK> --shift_trans_type rotation --load_path <MODEL_PATH>
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# Accuray & ECE
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python eval.py --mode test_marginalized_acc --dataset <DATASET> --model <NETWORK> --shift_trans_type rotation --load_path <MODEL_PATH>
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```
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> This option is for marginalized inference.
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> For single inference (also used for SupCLR) change --ood_score baseline in first command,
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> and --mode test_acc in second command.
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## 4. Results
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Our model achieves the following performance on:
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### One-Class Out-of-Distribution Detection
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| Method | Dataset | AUROC (Mean) |
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| --------------|------------------ | --------------|
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| SimCLR | CIFAR-10-OC | 87.9% |
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| Rot+Trans | CIFAR-10-OC | 90.0% |
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| CSI (ours) | CIFAR-10-OC | 94.3% |
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We only show CIFAR-10 one-class result in this repo. For other setting, please see our paper.
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### Unlabeled Multi-Class Out-of-Distribution Detection
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| Method | Dataset | OOD Dataset | AUROC (Mean) |
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| --------------|------------------ |---------------|--------------|
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| Rot+Trans | CIFAR-10 | CIFAR-100 | 82.5% |
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| CSI (ours) | CIFAR-10 | CIFAR-100 | 89.3% |
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We only show CIFAR-10 to CIFAR-100 OOD detection result in this repo. For other OOD dataset results, see our paper.
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### Labeled Multi-Class Result
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| Method | Dataset | OOD Dataset | Acc | ECE | AUROC (Mean) |
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| ---------------- |------------------ |---------------|-------|-------|--------------|
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| SupCLR | CIFAR-10 | CIFAR-100 | 93.9% | 5.54% | 88.3% |
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| CSI (ours) | CIFAR-10 | CIFAR-100 | 94.8% | 4.24% | 90.6% |
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| CSI-ensem (ours) | CIFAR-10 | CIFAR-100 | 96.0% | 3.64% | 92.3% |
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We only show CIFAR-10 with CIFAR-100 as OOD in this repo. For other dataset results, please see our paper.
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## 5. New OOD dataset
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<p align="center">
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<img src=figures/fixed_ood_benchmarks.png width="600">
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</p>
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We find that current benchmark datasets for OOD detection, are visually far from in-distribution datasets (e.g. CIFAR).
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To address this issue, we provide new datasets for OOD detection evaluation:
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[LSUN_fix](https://drive.google.com/file/d/1KVWj9xpHfVwGcErH5huVujk9snhEGOxE/view?usp=sharing),
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[ImageNet_fix](https://drive.google.com/file/d/1sO_-noq10mmziB1ECDyNhD5T4u5otyKA/view?usp=sharing).
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See the above figure for the visualization of current benchmark and our dataset.
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To generate OOD datasets, run the following codes inside the `./datasets` folder:
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```OOD dataset generation
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# ImageNet FIX generation code
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python imagenet_fix_preprocess.py
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# LSUN FIX generation code
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python lsun_fix_preprocess.py
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```
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## Citation
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```
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@inproceedings{tack2020csi,
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title={CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances},
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author={Jihoon Tack and Sangwoo Mo and Jongheon Jeong and Jinwoo Shin},
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booktitle={Advances in Neural Information Processing Systems},
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year={2020}
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}
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```
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Zum trainieren von Modellen: train.ipynb
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Zum evaluieren von Modellen: eval.ipynb
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Bereits trainierte Modelle in: ../logs/
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