Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 2: Strong rejection
review_content: The authors describe recent work where employing adversarial sample data can give significant improvement.

The authors work is not a generative adversarial network (GAN) nor training with adversarial examples, hence I would have difficulty labeling this paper as 'weak adversarial boosting'. In particular, note that adversarial examples are examples generated via gradient propagation in the model that perceptually indistinguishable to humans but are misclassified by the machine learning system. The data the authors describe are instead more like 'hard negatives' as humans judge that the algorithm incorrectly classified these examples.

The authors show that by employing these hard negatives and committee of experts they could improve the quality of the classifier. Both techniques of employing hard negatives and a committee of classifiers are known to be useful for training all sorts of machine learning systems and I do not see what is new in this work.

Additional issues:
- Authors have almost no references to prior work including but not limited to GAN's, adversarial examples, boosting, security issues, hard negative mining.
- Results based on non-publicly available data so not reproducible.
- Results are minimal on one data set and two experiments. 
review_title: Official review For Weak Adversarial Boosting

final_score: rating: 2: Strong rejection
review_content: This work proposes using a weak classifier to produce data to augment a supervised classifier. The way it does is poorly explained and seems similar to existing work on hard negative mining.

In section 3.1, does 'randomly sample outside B' mean sampling from A - B? Does A ∩ B mean the set of examples which were positively labeled by A and B? If this is the case I don't see how that would raise the precision of B to that of A, especially since the negatives that are being used to train B come from the positive set of A. This method of training seems very close to that described in section 3.2.

In particular in section 3.2, it is very unclear why using A ∩ B as the positive labels would increase the recall of B. Using the intersection would reduce the number of positive labels and seems like it would reduce the recall compared to the original.

The experimental results are also very weak. There is no description of 'correlated classifier' and no clear definition of what it means to be correlated. The authors also describe training on a random 1000 samples but 1000 samples of what? Since there are only 1000 labels, what are the results of training directly on the 1000 labeled examples? Are the 1000 positives from the low-precision classifier just examples from unlabelled data?

I also wouldn't describe these techniques as 'adversarial'. Adversarial is normally taken to mean something which intentionally exploits a weakness of the model. None of the described methods intentionally exploit any weakness.


review_title: Confusing description of methods and insufficient experiments

Decision: Reject