Semantic Noise Modeling for Better Representation Learning
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, generalization performance highly depends on the learned latent representation. In this work, we propose a novel latent space modeling method to learn better latent representation. We designed a neural network model based on the assumption that good base representation for supervised tasks can be attained by maximizing the sum of hierarchical mutual informations between the input, latent, and output variables. From this base model, we introduce a semantic noise modeling method which enables semantic perturbation on the latent space to enhance the representational power of learned latent feature. During training, latent vector representation can be stochastically perturbed by a modeled additive noise while preserving its original semantics. It implicitly brings the effect of semantic augmentation on the latent space. The proposed model can be easily learned by back-propagation with common gradient-based optimization algorithms. Experimental results show that the proposed method helps to achieve performance benefits against various previous approaches. We also provide the empirical analyses for the proposed latent space modeling method including t-SNE visualization.
The paper presents a new regularization technique for neural networks, which seeks to maximize correlation between input variables, latent variables and outputs. This is achieved by defining a measure of total correlation between these variables and decomposing it in terms of entropies and conditional entropies. Authors explain that they do not actually maximize the total correlation, but a lower-bound of it that ignores simple entropy terms, and only considers conditional entropies. It is not clearly explained what is the rationale for discarding these entropy terms. Entropies measures are applying to probability distributions (i.e. this implies that the variables in the model should be random). The link between the conditional entropy formulation and the reconstruction error is not made explicit. In order to link these two views, I would have expected, for example, a noise model for the units of the network. Later in the paper, it is claimed that the original ladder network is not suitable for supervised learning with small samples, and some empirical results seek to demonstrate this. But a more theoretical explanation why it is the case would have been welcome. The MNIST results are shown for a particular convolutional neural network architecture, however, most ladder network results for this dataset have been produced on standard fully-connected architectures. Results for such neural network architecture would have been desirable for more comparability with original ladder neural network results.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semantic Noise Modeling for Better Representation Learning
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, generalization performance highly depends on the learned latent representation. In this work, we propose a novel latent space modeling method to learn better latent representation. We designed a neural network model based on the assumption that good base representation for supervised tasks can be attained by maximizing the sum of hierarchical mutual informations between the input, latent, and output variables. From this base model, we introduce a semantic noise modeling method which enables semantic perturbation on the latent space to enhance the representational power of learned latent feature. During training, latent vector representation can be stochastically perturbed by a modeled additive noise while preserving its original semantics. It implicitly brings the effect of semantic augmentation on the latent space. The proposed model can be easily learned by back-propagation with common gradient-based optimization algorithms. Experimental results show that the proposed method helps to achieve performance benefits against various previous approaches. We also provide the empirical analyses for the proposed latent space modeling method including t-SNE visualization.
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 presents a new regularization technique for neural networks, which seeks to maximize correlation between input variables, latent variables and outputs. This is achieved by defining a measure of total correlation between these variables and decomposing it in terms of entropies and conditional entropies. Authors explain that they do not actually maximize the total correlation, but a lower-bound of it that ignores simple entropy terms, and only considers conditional entropies. It is not clearly explained what is the rationale for discarding these entropy terms. Entropies measures are applying to probability distributions (i.e. this implies that the variables in the model should be random). The link between the conditional entropy formulation and the reconstruction error is not made explicit. In order to link these two views, I would have expected, for example, a noise model for the units of the network. Later in the paper, it is claimed that the original ladder network is not suitable for supervised learning with small samples, and some empirical results seek to demonstrate this. But a more theoretical explanation why it is the case would have been welcome. The MNIST results are shown for a particular convolutional neural network architecture, however, most ladder network results for this dataset have been produced on standard fully-connected architectures. Results for such neural network architecture would have been desirable for more comparability with original ladder neural network results.
assistant
The paper presents a new regularization technique for neural networks, which seeks to maximize correlation between input variables, latent variables and outputs. This is achieved by defining a measure of total correlation between these variables and decomposing it in terms of entropies and conditional entropies. Authors explain that they do not actually maximize the total correlation, but a lower-bound of it that ignores simple entropy terms, and only considers conditional entropies. It is not clearly explained what is the rationale for discarding these entropy terms. Entropies measures are applying to probability distributions (i.e. this implies that the variables in the model should be random). The link between the conditional entropy formulation and the reconstruction error is not made explicit. In order to link these two views, I would have expected, for example, a noise model for the units of the network. Later in the paper, it is claimed that the original ladder network is not suitable for supervised learning with small samples, and some empirical results seek to demonstrate this. But a more theoretical explanation why it is the case would have been welcome. The MNIST results are shown for a particular convolutional neural network architecture, however, most ladder network results for this dataset have been produced on standard fully-connected architectures. Results for such neural network architecture would have been desirable for more comparability with original ladder neural network results.