Complement Objective Training
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.
 This paper considers augmenting the cross-entropy objective with "complement" objective maximization, which aims at neutralizing the predicted probabilities of classes other than the ground truth one. The main idea is to help the ground truth label stands out more easily by smoothing out potential peaks in non-ground-truth labels. The wide application of the cross-entropy objective makes this approach applicable to many different machine/deep learning applications varying from computer vision to NLP. The paper is well-written, with a clear explanation for the motivation of introducing the complement entropy objective and several good visualization of its empirical effects (e.g., Figures 1 and 2). The numerical experiments also incorporate a wide spectrum of applications and network structures as well as dataset sizes, and the performance improvement is quite impressive and consistent. In particular, the adversarial attacks example looks very interesting. One small suggestion is that the authors can also make some comments on the connection between the two-step update algorithm (Algorithm 1) with multi-objective optimization. In particular, I would suggest the authors also try some multi-objective optimization techniques apart from the simple but effective heuristics, and see if some Pareto-optimality can be guaranteed and better practical improvement can be achieved.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Complement Objective Training
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.
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 considers augmenting the cross-entropy objective with "complement" objective maximization, which aims at neutralizing the predicted probabilities of classes other than the ground truth one. The main idea is to help the ground truth label stands out more easily by smoothing out potential peaks in non-ground-truth labels. The wide application of the cross-entropy objective makes this approach applicable to many different machine/deep learning applications varying from computer vision to NLP. The paper is well-written, with a clear explanation for the motivation of introducing the complement entropy objective and several good visualization of its empirical effects (e.g., Figures 1 and 2). The numerical experiments also incorporate a wide spectrum of applications and network structures as well as dataset sizes, and the performance improvement is quite impressive and consistent. In particular, the adversarial attacks example looks very interesting. One small suggestion is that the authors can also make some comments on the connection between the two-step update algorithm (Algorithm 1) with multi-objective optimization. In particular, I would suggest the authors also try some multi-objective optimization techniques apart from the simple but effective heuristics, and see if some Pareto-optimality can be guaranteed and better practical improvement can be achieved.
assistant
 This paper considers augmenting the cross-entropy objective with "complement" objective maximization, which aims at neutralizing the predicted probabilities of classes other than the ground truth one. The main idea is to help the ground truth label stands out more easily by smoothing out potential peaks in non-ground-truth labels. The wide application of the cross-entropy objective makes this approach applicable to many different machine/deep learning applications varying from computer vision to NLP. The paper is well-written, with a clear explanation for the motivation of introducing the complement entropy objective and several good visualization of its empirical effects (e.g., Figures 1 and 2). The numerical experiments also incorporate a wide spectrum of applications and network structures as well as dataset sizes, and the performance improvement is quite impressive and consistent. In particular, the adversarial attacks example looks very interesting. One small suggestion is that the authors can also make some comments on the connection between the two-step update algorithm (Algorithm 1) with multi-objective optimization. In particular, I would suggest the authors also try some multi-objective optimization techniques apart from the simple but effective heuristics, and see if some Pareto-optimality can be guaranteed and better practical improvement can be achieved.