Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: The paper proposes a hybrid network architecture that can integrate features extracted via supervised training and unsupervised neuro-inspired learning. The paper is well-written and the experimental results seem sensible. The experimental results mainly revolve around testing the networks over noise added to training images.  The problem of image denoising is very well-studied and very good methods have been proposed for image denoising under arbitrary noise using deep learning (see the works in CVPR, ICCV, ECCV etc.). Unfortunately, I am not in the position to judge the novelty 
wrt spiking neuron network literature. Nevertheless, as far as computer vision or general applications is concerned the proposed pipeline would not be among the methods of choice.  Hence, I am recommending weak reject for now, waiting for a more informed opinion to see if I will change my opinion.  
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: The paper shows that replacing feature extraction layers by spiking convolution network can improve the performance under random noise. The algorithm itself is simple since it's just a combiniation of STDP and standard CNN. The results shows improved performance under some random noise. Although the idea is cute, I feel the paper fails to convince why spiking nets are more robust to random noise; the explanation using backprop rules in section 3 sounds interesting but does not fully convince me; for example, if we train a CNN by other approach instead of back-propagation, can we also improve robustness to input noise? Also, what kind of input noise are we considering in the analysis? 

Also, I have some questions on the experiments: 

1. Experiments are only tested under one kind of random perturbation with different strengths. I think it will be better if the algorithm can consistently improve over various kinds of noise distributions. 

2. It is mentioned in the introduction that some methods were proposed to filter out the input noise, but they are not compared in the experiments. 

3. What's the training time of the proposed method? 
review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: The paper develops a method to augment deep neural networks with Spike-Time-Dependent-Plasticity (STDP) aiming at improving noise robustness of the network learned features. In the hybrid network, learned feature is the concatenation of traditionally supervised-ly learned features and those from an auxiliary module trained locally and unsupervised-ly by STDP. The new network demonstrates improved noise robustness via improved classification accuracy on Cifar10 and ImageNet subset when input data have noise, on different network architectures.

The paper, however, fails to address the many works in the literature about adversarial perturbations ('attack') and adversarial training ('defense'), starting by (Szegedy et al., 2013). The different types of attacks affect the efficiency of defense due to the game-theoretical nature of the adversarial perturbation problem. If the attack is blind to the classification model, e.g., Gaussian attack by adding Gaussian noise, then image restoration techniques like denoising could provide an effective 'defense'. Thus model-specific attacks are of more application interest than model-blind ones. The current manuscript did not address the specific noise type being used to perturb the image. It is unlikely that the local learning techniques proposed in the paper can work on many kinds of perturbations especially the 'attacks' which is model specific. 

The proposed methodology is a feature concatenation of local (low-level) features of image data and deep features. Given the current state of the manuscript, the level of methodological novelty and the scope of input perturbations that can be made robust against both appear to be limited.

References:
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013.

review_title: Official Blind Review #2

Meta-review:  The paper proposes to improve noise robustness of the network learned features, by augmenting deep networks with Spike-Time-Dependent-Plasticity (STDP). The new network show improved noise robustness with better classification accuracy on Cifar10 and ImageNet subset when input data have noise. While this paper is well written, a number of concerns are raised by the reviewers. They include that the proposed method would not be favored from computer vision perspective, it is not convincing why spiking nets are more robust to random noises, and the method fails to address works in adversarial perturbations and adversarial training. Also, Reviewer #2 pointed out the low level of methodological novelty. The authors provided response to the questions, but did not change the rating of the reviewers. Given the various concerns raised, the ACs recommend reject.
Decision: Reject