ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
Context Aware Advertisement (CAA) is a type of advertisement appearing on websites or mobile apps. The advertisement is targeted on specific group of users and/or the content displayed on the websites or apps. This paper focuses on classifying images displayed on the websites by incremental learning classifier with Deep Convolutional Neural Network (DCNN) especially for Context Aware Advertisement (CAA) framework. Incrementally learning new knowledge with DCNN leads to catastrophic forgetting as previously stored information is replaced with new information. To prevent catastrophic forgetting, part of previously learned knowledge should be stored for the life time of incremental classifier. Storing information for life time involves privacy and legal concerns especially in context aware advertising framework. Here, we propose an incremental classifier learning method which addresses privacy and legal concerns while taking care of catastrophic forgetting problem. We conduct experiments on different datasets including CIFAR-100. Experimental results show that proposed system achieves relatively high performance compared to the state-of-the-art incremental learning methods.
 The paper extends an existing incremental learning method, mainly introducing the latent representations of an autoencoder instead of the original images. It includes a lot of hype in that it simulates the human brain - because it is based on the iCaRL & Fear Net formulation - and that it fulfils the privacy and legal requirements - because it stores and uses the auto-encoder representations instead of the images. Specific comments: - The title of the paper defines its topic to be context aware advertisement, whereas the main results and all comparisons are made on the CIFAR dataset. Only the last Table (5) provides the performance on the IMDB-CAA dataset, without any detailed analysis of the experiments. - The results in Table 3 are quite strange: the presented approach starts by outperforming iCaRL method, but then deteriorates very fast wrt size and is much lower than the original method, with no justification on this. Some improvement is shown in size 16.6% without, again, any logical explanation provided. - Section 4.2 does not provide any detail of the integration resulting in the presented system; Fig.2 does not provide a clear description either. - Language improvement is required in the experimental sections. -
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
Context Aware Advertisement (CAA) is a type of advertisement appearing on websites or mobile apps. The advertisement is targeted on specific group of users and/or the content displayed on the websites or apps. This paper focuses on classifying images displayed on the websites by incremental learning classifier with Deep Convolutional Neural Network (DCNN) especially for Context Aware Advertisement (CAA) framework. Incrementally learning new knowledge with DCNN leads to catastrophic forgetting as previously stored information is replaced with new information. To prevent catastrophic forgetting, part of previously learned knowledge should be stored for the life time of incremental classifier. Storing information for life time involves privacy and legal concerns especially in context aware advertising framework. Here, we propose an incremental classifier learning method which addresses privacy and legal concerns while taking care of catastrophic forgetting problem. We conduct experiments on different datasets including CIFAR-100. Experimental results show that proposed system achieves relatively high performance compared to the state-of-the-art incremental learning methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper extends an existing incremental learning method, mainly introducing the latent representations of an autoencoder instead of the original images. It includes a lot of hype in that it simulates the human brain - because it is based on the iCaRL & Fear Net formulation - and that it fulfils the privacy and legal requirements - because it stores and uses the auto-encoder representations instead of the images. Specific comments: - The title of the paper defines its topic to be context aware advertisement, whereas the main results and all comparisons are made on the CIFAR dataset. Only the last Table (5) provides the performance on the IMDB-CAA dataset, without any detailed analysis of the experiments. - The results in Table 3 are quite strange: the presented approach starts by outperforming iCaRL method, but then deteriorates very fast wrt size and is much lower than the original method, with no justification on this. Some improvement is shown in size 16.6% without, again, any logical explanation provided. - Section 4.2 does not provide any detail of the integration resulting in the presented system; Fig.2 does not provide a clear description either. - Language improvement is required in the experimental sections. -
assistant
 The paper extends an existing incremental learning method, mainly introducing the latent representations of an autoencoder instead of the original images. It includes a lot of hype in that it simulates the human brain - because it is based on the iCaRL & Fear Net formulation - and that it fulfils the privacy and legal requirements - because it stores and uses the auto-encoder representations instead of the images. Specific comments: - The title of the paper defines its topic to be context aware advertisement, whereas the main results and all comparisons are made on the CIFAR dataset. Only the last Table (5) provides the performance on the IMDB-CAA dataset, without any detailed analysis of the experiments. - The results in Table 3 are quite strange: the presented approach starts by outperforming iCaRL method, but then deteriorates very fast wrt size and is much lower than the original method, with no justification on this. Some improvement is shown in size 16.6% without, again, any logical explanation provided. - Section 4.2 does not provide any detail of the integration resulting in the presented system; Fig.2 does not provide a clear description either. - Language improvement is required in the experimental sections. -