Anomaly Detection via Reverse Distillation from One-Class Embedding

Hi, sir. Thanks for your great job, I have some confuse about OCBE ：

MFF aligns multi-scale features from teacher E and OCE condenses the obtained rich feature to a compact bottleneck code. But, the MMF part can did all things above. Why still use OCE module? Table 5 shows ablation study on Pre, Pre+OCE, Pre+OCE+MFF.
did you do the ablation study on pre+MFF?

OCBE module condenses the multi-scale patterns into an extreme low-dimensional space for downstream normal representation reconstruction. Then, the abnormal representations generated by the teacher model are likely to be abandoned by OCBE. why？？？？

I am looking forward to your reply! Thanks.

Dear Author I want to do the ablation study of your network ,but i have some question about it 。 if I want to use the single-layer features M1, how should I do the ablation study,like the first pic below or the latter one?

Hi, first I was deeply impressed by your model, and I have a question

Is auroc_sp the number of classification?

Can this model perform classication?

Thank you for your wonderful work and I look forward to your reply.

Thanks for releasing the official implementation! I am working on integrating Reverse Distillation model into Anomalib. Can you provide a license for your code?

Thanks for your great job, I have some questions： 1.the MVTecDataset has about 17 classes data, the code shows every class train and save a model, do you try to train a model can work on all classes data? 2.how to train One-Class Novelty Detection with cifar10 and mnist, just train with one class data, the others for test as anomaly? could you show the code? 3.How to predict a picture and judge whether it is abnormal？ inputs = encoder(img) outputs = decoder(bn(inputs))#bn(inputs)) loss = loss_fucntion(inputs, outputs) judge whether it is abnormal with loss? and the the threshold? 4. the Official Code not save the encoder model, the encoder parameter just use the pretrain model and not update?
torch.save({'bn': bn.state_dict(), 'decoder': decoder.state_dict()}, ckp_path) many thanks

Traceback (most recent call last): File "/media/pmj/e/code/anomaly_code/RD4AD-main/main.py", line 121, in train(i) File "/media/pmj/e/code/anomaly_code/RD4AD-main/main.py", line 105, in train auroc_px, auroc_sp, aupro_px = evaluation(encoder, bn, decoder, test_dataloader, device) File "/media/pmj/e/code/anomaly_code/RD4AD-main/test.py", line 89, in evaluation anomaly_map[np.newaxis,:,:])) File "/media/pmj/e/code/anomaly_code/RD4AD-main/test.py", line 355, in compute_pro df = pd.DataFrame([], columns=["pro", "fpr", "threshold"]) File "/home/pmj/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 490, in init mgr = init_dict({}, index, columns, dtype=dtype) File "/home/pmj/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 239, in init_dict val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype) File "/home/pmj/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 1449, in construct_1d_arraylike_from_scalar dtype = dtype.dtype AttributeError: type object 'object' has no attribute 'dtype'

hello

assert set(masks.flatten()) == {0, 1}, "set(masks.flatten()) must be {0, 1}" AssertionError: set(masks.flatten()) must be {0, 1}

This error occurs when testing. how should we solve it? But our image is already a binary map, so this problem should not occur.

I would like to ask why the multi-scale anomaly score map can directly calculate the AUROC with the ground truth. What is the relationship between the anomalies represented by the feature similarity output of the network layer and the anomalies shown by the annotations?