Top-down training for neural networks
Vanishing gradients pose a challenge when training deep neural networks, resulting in the top layers (closer to the output) in the network learning faster when compared with lower layers closer to the input. Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor. This can lead to the feature extractor being under-trained, possibly failing to learn much about the patterns in the input data. To address this we propose a good classifier hypothesis: given a fixed classifier that partitions the space well, the feature extractor can be further trained to fit that classifier and learn the data patterns well. This alleviates the problem of under-training the feature extractor and enables the network to learn patterns in the data with small partial derivatives. We verify this hypothesis empirically and propose a novel top-down training method. We train all layers jointly, obtaining a good classifier from the top layers, which are then frozen. Following re-initialization, we retrain the bottom layers with respect to the frozen classifier. Applying this approach to a set of speech recognition experiments using the Wall Street Journal and noisy CHiME-4 datasets we observe substantial accuracy gains. When combined with dropout, our method enables connectionist temporal classification (CTC) models to outperform joint CTC-attention models, which have more capacity and flexibility.
This work proposed a mechanism to freeze top layers after supervised pre-training, and re-initialize and retrain the bottom layers. For a model with n layers, when a separation index i is specified, the approach define layer 1~i as bottom layers and i+1~n as top layers. The proposed process enumerate all i from 1 to n-1, compute resulting validation errors respectively, and then pick the i with lowest validation error. The algorithm exhibited significant improvement on WSJ and some minor improvement on CHiME-4. This work provides some new insight for training ASRs and the observations provide further data points for understanding the training behavior. The layer freezing trick however is relatively well-known, and thus leaving the novelty of the proposed idea to be limited at what layers they choose to freeze. In algorithm 1 it describes the mechanism as having two loops while it really only needs one loop. The author mentioned they used a simplified version later in the text, and I’ll suggest to update the algorithm block to make it clearer.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Top-down training for neural networks
Vanishing gradients pose a challenge when training deep neural networks, resulting in the top layers (closer to the output) in the network learning faster when compared with lower layers closer to the input. Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor. This can lead to the feature extractor being under-trained, possibly failing to learn much about the patterns in the input data. To address this we propose a good classifier hypothesis: given a fixed classifier that partitions the space well, the feature extractor can be further trained to fit that classifier and learn the data patterns well. This alleviates the problem of under-training the feature extractor and enables the network to learn patterns in the data with small partial derivatives. We verify this hypothesis empirically and propose a novel top-down training method. We train all layers jointly, obtaining a good classifier from the top layers, which are then frozen. Following re-initialization, we retrain the bottom layers with respect to the frozen classifier. Applying this approach to a set of speech recognition experiments using the Wall Street Journal and noisy CHiME-4 datasets we observe substantial accuracy gains. When combined with dropout, our method enables connectionist temporal classification (CTC) models to outperform joint CTC-attention models, which have more capacity and flexibility.
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 work proposed a mechanism to freeze top layers after supervised pre-training, and re-initialize and retrain the bottom layers. For a model with n layers, when a separation index i is specified, the approach define layer 1~i as bottom layers and i+1~n as top layers. The proposed process enumerate all i from 1 to n-1, compute resulting validation errors respectively, and then pick the i with lowest validation error. The algorithm exhibited significant improvement on WSJ and some minor improvement on CHiME-4. This work provides some new insight for training ASRs and the observations provide further data points for understanding the training behavior. The layer freezing trick however is relatively well-known, and thus leaving the novelty of the proposed idea to be limited at what layers they choose to freeze. In algorithm 1 it describes the mechanism as having two loops while it really only needs one loop. The author mentioned they used a simplified version later in the text, and I’ll suggest to update the algorithm block to make it clearer.
assistant
This work proposed a mechanism to freeze top layers after supervised pre-training, and re-initialize and retrain the bottom layers. For a model with n layers, when a separation index i is specified, the approach define layer 1~i as bottom layers and i+1~n as top layers. The proposed process enumerate all i from 1 to n-1, compute resulting validation errors respectively, and then pick the i with lowest validation error. The algorithm exhibited significant improvement on WSJ and some minor improvement on CHiME-4. This work provides some new insight for training ASRs and the observations provide further data points for understanding the training behavior. The layer freezing trick however is relatively well-known, and thus leaving the novelty of the proposed idea to be limited at what layers they choose to freeze. In algorithm 1 it describes the mechanism as having two loops while it really only needs one loop. The author mentioned they used a simplified version later in the text, and I’ll suggest to update the algorithm block to make it clearer.