DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this paper, we propose a novel regularized transfer learning framework DELTA, namely DEep Learning Transfer using Feature Map with Attention. Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network. Specifically, in addition to minimizing the empirical loss, DELTA intends to align the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in an supervised learning manner. We evaluate DELTA with the state-of-the-art algorithms, including L2 and L2-SP. The experiment results show that our proposed method outperforms these baselines with higher accuracy for new tasks.
 Summary The paper describes using the technique of modifying the weights for the outer layers, used in teacher-student network for same task, to transfer learning for different tasks by modifying the loss function and pre-training using target network labels to emphasize the neurons that are considered important for prediction. The technique seems to be no more/slightly better than the Lsquare SP, but exceeds when used with attention. Improvements - the amount of training time needed to pre-train using the L-square FE and target labels should be mentioned as it seems that for large network, and large data, this can be a factor - The choice of Resnet, at least one of the more recent networks for object detection (Inception, YOLO etc.) would be a good add
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this paper, we propose a novel regularized transfer learning framework DELTA, namely DEep Learning Transfer using Feature Map with Attention. Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network. Specifically, in addition to minimizing the empirical loss, DELTA intends to align the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in an supervised learning manner. We evaluate DELTA with the state-of-the-art algorithms, including L2 and L2-SP. The experiment results show that our proposed method outperforms these baselines with higher accuracy for new tasks.
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<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>



 Summary The paper describes using the technique of modifying the weights for the outer layers, used in teacher-student network for same task, to transfer learning for different tasks by modifying the loss function and pre-training using target network labels to emphasize the neurons that are considered important for prediction. The technique seems to be no more/slightly better than the Lsquare SP, but exceeds when used with attention. Improvements - the amount of training time needed to pre-train using the L-square FE and target labels should be mentioned as it seems that for large network, and large data, this can be a factor - The choice of Resnet, at least one of the more recent networks for object detection (Inception, YOLO etc.) would be a good add
assistant
 Summary The paper describes using the technique of modifying the weights for the outer layers, used in teacher-student network for same task, to transfer learning for different tasks by modifying the loss function and pre-training using target network labels to emphasize the neurons that are considered important for prediction. The technique seems to be no more/slightly better than the Lsquare SP, but exceeds when used with attention. Improvements - the amount of training time needed to pre-train using the L-square FE and target labels should be mentioned as it seems that for large network, and large data, this can be a factor - The choice of Resnet, at least one of the more recent networks for object detection (Inception, YOLO etc.) would be a good add