Continual Learning using the SHDL Framework with Skewed Replay Distributions
Human and animals continuously acquire, adapt as well as transfer knowledge throughout their lifespan. The ability to learn continuously is crucial for the effective functioning of agents interacting with the real world and processing continuous streams of information. Continuous learning has been a long-standing challenge for neural networks as the repeated acquisition of information from non-uniform data distributions generally lead to catastrophic forgetting or interference. This work proposes a modular architecture capable of continuous acquisition of tasks while averting catastrophic forgetting. Specifically, our contributions are: (i) Efficient Architecture: a modular architecture emulating the visual cortex that can learn meaningful representations with limited labelled examples, (ii) Knowledge Retention: retention of learned knowledge via limited replay of past experiences, (iii) Forward Transfer: efficient and relatively faster learning on new tasks, and (iv) Naturally Skewed Distributions: The learning in the above-mentioned claims is performed on non-uniform data distributions which better represent the natural statistics of our ongoing experience. Several experiments that substantiate the above-mentioned claims are demonstrated on the CIFAR-100 dataset.
The paper suggest to use the previously proposed ScatterNet Hybrid Deep Learning (SHDL) network in a continual learning setting. This is motivated by the fact that the SHDL needs less supervised data, so keeping a small replay buffer can be enough to maintain performance while avoiding catastrophic forgetting. My main doubt is the benchmark and evaluation of the proposed method. The metrics reported are all relative to a baseline value (which I could not find reported), and make it difficult to understand how the model is performing in absolute term. This is particularly a problem when comparing with existing state of the art method (Fig. 3, Table 4), since this does not exclude that they may have an overall much better accuracy in absolute terms. Also concerning the comparison with the previous literature, I could find no details about the architecture and the training algorithm used. Notice that this may in particular affect some the reported metrics, since they depend on the shape of the training curve (reporting the training curves for all methods may also be useful). Also, since SHDL uses a small replay buffer, are EWC and the other method modified to use the replay buffer and make the comparison fair? While several standard tests for continual learning exists (for example the split CIFAR10/100 in Zenke et al., 2017), those are not used, and rather a simpler test is used which only attempt to learn continually two datasets. It would be helpful to also report a direct comparison on those tests. Regarding the line: *The autoencoder is jointly trained from scratch for classes of both phases to learn mid-level features*, does this mean that the auto-encoder is trained using data of the two distributions at the same time rather than one after the other? If it is the former case, while it is unsupervised training, it would be a deviation from the standard continual learning framework and should clearly be stated.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Continual Learning using the SHDL Framework with Skewed Replay Distributions
Human and animals continuously acquire, adapt as well as transfer knowledge throughout their lifespan. The ability to learn continuously is crucial for the effective functioning of agents interacting with the real world and processing continuous streams of information. Continuous learning has been a long-standing challenge for neural networks as the repeated acquisition of information from non-uniform data distributions generally lead to catastrophic forgetting or interference. This work proposes a modular architecture capable of continuous acquisition of tasks while averting catastrophic forgetting. Specifically, our contributions are: (i) Efficient Architecture: a modular architecture emulating the visual cortex that can learn meaningful representations with limited labelled examples, (ii) Knowledge Retention: retention of learned knowledge via limited replay of past experiences, (iii) Forward Transfer: efficient and relatively faster learning on new tasks, and (iv) Naturally Skewed Distributions: The learning in the above-mentioned claims is performed on non-uniform data distributions which better represent the natural statistics of our ongoing experience. Several experiments that substantiate the above-mentioned claims are demonstrated on the CIFAR-100 dataset.
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 suggest to use the previously proposed ScatterNet Hybrid Deep Learning (SHDL) network in a continual learning setting. This is motivated by the fact that the SHDL needs less supervised data, so keeping a small replay buffer can be enough to maintain performance while avoiding catastrophic forgetting. My main doubt is the benchmark and evaluation of the proposed method. The metrics reported are all relative to a baseline value (which I could not find reported), and make it difficult to understand how the model is performing in absolute term. This is particularly a problem when comparing with existing state of the art method (Fig. 3, Table 4), since this does not exclude that they may have an overall much better accuracy in absolute terms. Also concerning the comparison with the previous literature, I could find no details about the architecture and the training algorithm used. Notice that this may in particular affect some the reported metrics, since they depend on the shape of the training curve (reporting the training curves for all methods may also be useful). Also, since SHDL uses a small replay buffer, are EWC and the other method modified to use the replay buffer and make the comparison fair? While several standard tests for continual learning exists (for example the split CIFAR10/100 in Zenke et al., 2017), those are not used, and rather a simpler test is used which only attempt to learn continually two datasets. It would be helpful to also report a direct comparison on those tests. Regarding the line: *The autoencoder is jointly trained from scratch for classes of both phases to learn mid-level features*, does this mean that the auto-encoder is trained using data of the two distributions at the same time rather than one after the other? If it is the former case, while it is unsupervised training, it would be a deviation from the standard continual learning framework and should clearly be stated.
assistant
The paper suggest to use the previously proposed ScatterNet Hybrid Deep Learning (SHDL) network in a continual learning setting. This is motivated by the fact that the SHDL needs less supervised data, so keeping a small replay buffer can be enough to maintain performance while avoiding catastrophic forgetting. My main doubt is the benchmark and evaluation of the proposed method. The metrics reported are all relative to a baseline value (which I could not find reported), and make it difficult to understand how the model is performing in absolute term. This is particularly a problem when comparing with existing state of the art method (Fig. 3, Table 4), since this does not exclude that they may have an overall much better accuracy in absolute terms. Also concerning the comparison with the previous literature, I could find no details about the architecture and the training algorithm used. Notice that this may in particular affect some the reported metrics, since they depend on the shape of the training curve (reporting the training curves for all methods may also be useful). Also, since SHDL uses a small replay buffer, are EWC and the other method modified to use the replay buffer and make the comparison fair? While several standard tests for continual learning exists (for example the split CIFAR10/100 in Zenke et al., 2017), those are not used, and rather a simpler test is used which only attempt to learn continually two datasets. It would be helpful to also report a direct comparison on those tests. Regarding the line: *The autoencoder is jointly trained from scratch for classes of both phases to learn mid-level features*, does this mean that the auto-encoder is trained using data of the two distributions at the same time rather than one after the other? If it is the former case, while it is unsupervised training, it would be a deviation from the standard continual learning framework and should clearly be stated.