Interactive Boosting of Neural Networks for Small-sample Image Classification
Neural networks have recently shown excellent performance on numerous classi- fication tasks. These networks often have a large number of parameters and thus require much data to train. When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, resulting in a large model variance and a poor generalization performance. To address this problem, we propose a new ensemble learning method called InterBoost for small-sample image classification. In the training phase, InterBoost first randomly generates two complementary datasets to train two base networks of the same structure, separately, and then next two complementary datasets for further training the networks are generated through interaction (or information sharing) between the two base networks trained previously. This interactive training process continues iteratively until a stop criterion is met. In the testing phase, the outputs of the two networks are combined to obtain one final score for classification. Detailed analysis of the method is provided for an in-depth understanding of its mechanism.
 This paper proposes interactive boosting to train two complimentary networks for relative small image classification. This is an interesting idea for small sample training, although I am not sure whether it can be applied to "really" small sample datasets since UIUC or LabelMe contains at least thousands of images. The experimental results are not very convincing, and I wish the final paper could compare with more baselines, including 1. adapting pretrained model (e.g., from ImageNet) to the proposed problem 2. deep CNN with dropout 3. deep CNN with distillation 4. deep CNN with data augmentation which all are popular techniques to apply deep network for small sample problems.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Interactive Boosting of Neural Networks for Small-sample Image Classification
Neural networks have recently shown excellent performance on numerous classi- fication tasks. These networks often have a large number of parameters and thus require much data to train. When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, resulting in a large model variance and a poor generalization performance. To address this problem, we propose a new ensemble learning method called InterBoost for small-sample image classification. In the training phase, InterBoost first randomly generates two complementary datasets to train two base networks of the same structure, separately, and then next two complementary datasets for further training the networks are generated through interaction (or information sharing) between the two base networks trained previously. This interactive training process continues iteratively until a stop criterion is met. In the testing phase, the outputs of the two networks are combined to obtain one final score for classification. Detailed analysis of the method is provided for an in-depth understanding of its mechanism.
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>



 This paper proposes interactive boosting to train two complimentary networks for relative small image classification. This is an interesting idea for small sample training, although I am not sure whether it can be applied to "really" small sample datasets since UIUC or LabelMe contains at least thousands of images. The experimental results are not very convincing, and I wish the final paper could compare with more baselines, including 1. adapting pretrained model (e.g., from ImageNet) to the proposed problem 2. deep CNN with dropout 3. deep CNN with distillation 4. deep CNN with data augmentation which all are popular techniques to apply deep network for small sample problems.
assistant
 This paper proposes interactive boosting to train two complimentary networks for relative small image classification. This is an interesting idea for small sample training, although I am not sure whether it can be applied to "really" small sample datasets since UIUC or LabelMe contains at least thousands of images. The experimental results are not very convincing, and I wish the final paper could compare with more baselines, including 1. adapting pretrained model (e.g., from ImageNet) to the proposed problem 2. deep CNN with dropout 3. deep CNN with distillation 4. deep CNN with data augmentation which all are popular techniques to apply deep network for small sample problems.