ENHANCING THE RELIABILITY OF OUT-OF-DISTRIBUTION IMAGE DETECTION IN NEURAL NETWORKS
We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in-and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach (Hendrycks & Gimpel, 2017) by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10 and Tiny-ImageNet) when the true positive rate is 95%.
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The authors addressed my concerns satisfactorily. Given this and the other reviews I have bumped up my score from a 5 to a 6.
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This paper introduces two modifications that allow neural networks to be better at distinguishing between in- and out- of distribution examples: (i) adding a high temperature to the softmax, and (ii) adding adversarial perturbations to the inputs. This is a novel use of existing methods.
Some roughly chronological comments follow:
In the abstract you don't mention that the result given is when CIFAR-10 is mixed with TinyImageNet.
The paper is quite well written aside from some grammatical issues. In particular, articles are frequently missing from nouns. Some sentences need rewriting (e.g. in 4.1 "which is as well used by Hendrycks...", in 5.2 "performance becomes unchanged").
 It is perhaps slightly unnecessary to give a name to your approach (ODIN) but in a world where there are hundreds of different kinds of GANs you could be forgiven.
I'm not convinced that the performance of the network for in-distribution images is unchanged, as this would require you to be able to isolate 100% of the in-distribution images. I'm curious as to what would happen to the overall accuracy if you ignored the results for in-distribution images that appear to be out-of-distribution (e.g. by simply counting them as incorrect classifications). Would there be a correlation between difficult-to-classify images, and those that don't appear to be in distribution?
When you describe the method it relies on a threshold delta which does not appear to be explicitly mentioned again.
In terms of experimentation it would be interesting to see the reciprocal of the results between two datasets. For instance, how would a network trained on TinyImageNet cope with out-of-distribution images from CIFAR 10?
Section 4.5 felt out of place, as to me, the discussion section flowed more naturally from the experimental results. This may just be a matter of taste.
I did like the observations in 5.1 about class deviation, although then, what would happen if the out-of-distribution dataset had a similar class distribution to the in-distribution one? (This is in part, addressed in the CIFAR80 20 experiments in the appendices).
This appears to be a borderline paper, as I am concerned that the method isn't sufficiently novel (although it is a novel use of existing methods).
Pros:
- Baseline performance is exceeded by a large margin
- Novel use of adversarial perturbation and temperature
- Interesting analysis
Cons:
- Doesn't introduce and novel methods of its own
- Could do with additional experiments (as mentioned above)
- Minor grammatical errors