Dataset Distillation
Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called {em dataset distillation}: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to {em synthesize} a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress MNIST training images into just synthetic {em distilled images} (one per class) and achieve close to original performance with only a few steps of gradient descent, given a particular fixed network initialization. We evaluate our method in a wide range of initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods in most settings.
 The paper addresses the interesting problem of generating a small number of synthetic examples that can be used to train a classifier, replacing a larger dataset. The paper is clearly written, the approach makes sense, and the experiments are interesting. My major concerns are regarding to previous literature, analysis of the algorithm, and details of the experiment. Overall, I expect an ICLR paper to go deeper (rather than wide). I recommend presenting strong convincing evidence on one front. Specific comments: (1) I*m missing analysis of the proposed procedure. It wasn*t fully clear which Loss it minimizes and if it indeed guaranteed to converge to the minimum of that loss. (2) The topic of learning from few samples is presented as completely new. It is well known that for classical linear algorithms like the Perceptron and SVM, the weights are a weighted sum of (label-weighted) samples, hence by definition of these algorithm, there is a single sample that can be used to "train" the model in one step. I*d expect some discussion of how the proposed approach relate to these classical approaches. There is also existing literature on a related problem of selecting samples (Teaching dimension Goldman&Kearns) that could be somewhat relevant here. (3) Motivation. The paper provide several motivations for dataset distillation. I support the first motivation of scientific understanding what data is actually needed for a classifier, and this means that deeper analysis is needed. The practical motivations are less convincing, because (a) domain adaptation experiments are not compared with real baselines (b) robustness of poisoning with a single sample is not studied/discussed. (4) experiments: The intro states that training with 10 images reaches 94% accuracy, but this does not seem consistent with the results in Table 1. The caption of figure 2 suggests that accuracy is between 12% and 94% which means the stated 94% is not representative or typical. Could you clarify? For domain adaptation. The baseline (random images) are very weak, and still perform almost comparably to the proposed approach. More robust experiments are needed here: stronger baselines, decent hyper-parameter search etc. (5) Writing and exposition: The paper addresses two issues: (a) learning with few synthetic samples, and (b) learning with few gradient steps. The intro tends to mix the two, and it is not clear why learning with a single gradient step is important. I recommend to separate the two topics more clearly.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dataset Distillation
Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called {em dataset distillation}: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to {em synthesize} a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress MNIST training images into just synthetic {em distilled images} (one per class) and achieve close to original performance with only a few steps of gradient descent, given a particular fixed network initialization. We evaluate our method in a wide range of initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods in most settings.
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 addresses the interesting problem of generating a small number of synthetic examples that can be used to train a classifier, replacing a larger dataset. The paper is clearly written, the approach makes sense, and the experiments are interesting. My major concerns are regarding to previous literature, analysis of the algorithm, and details of the experiment. Overall, I expect an ICLR paper to go deeper (rather than wide). I recommend presenting strong convincing evidence on one front. Specific comments: (1) I*m missing analysis of the proposed procedure. It wasn*t fully clear which Loss it minimizes and if it indeed guaranteed to converge to the minimum of that loss. (2) The topic of learning from few samples is presented as completely new. It is well known that for classical linear algorithms like the Perceptron and SVM, the weights are a weighted sum of (label-weighted) samples, hence by definition of these algorithm, there is a single sample that can be used to "train" the model in one step. I*d expect some discussion of how the proposed approach relate to these classical approaches. There is also existing literature on a related problem of selecting samples (Teaching dimension Goldman&Kearns) that could be somewhat relevant here. (3) Motivation. The paper provide several motivations for dataset distillation. I support the first motivation of scientific understanding what data is actually needed for a classifier, and this means that deeper analysis is needed. The practical motivations are less convincing, because (a) domain adaptation experiments are not compared with real baselines (b) robustness of poisoning with a single sample is not studied/discussed. (4) experiments: The intro states that training with 10 images reaches 94% accuracy, but this does not seem consistent with the results in Table 1. The caption of figure 2 suggests that accuracy is between 12% and 94% which means the stated 94% is not representative or typical. Could you clarify? For domain adaptation. The baseline (random images) are very weak, and still perform almost comparably to the proposed approach. More robust experiments are needed here: stronger baselines, decent hyper-parameter search etc. (5) Writing and exposition: The paper addresses two issues: (a) learning with few synthetic samples, and (b) learning with few gradient steps. The intro tends to mix the two, and it is not clear why learning with a single gradient step is important. I recommend to separate the two topics more clearly.
assistant
 The paper addresses the interesting problem of generating a small number of synthetic examples that can be used to train a classifier, replacing a larger dataset. The paper is clearly written, the approach makes sense, and the experiments are interesting. My major concerns are regarding to previous literature, analysis of the algorithm, and details of the experiment. Overall, I expect an ICLR paper to go deeper (rather than wide). I recommend presenting strong convincing evidence on one front. Specific comments: (1) I*m missing analysis of the proposed procedure. It wasn*t fully clear which Loss it minimizes and if it indeed guaranteed to converge to the minimum of that loss. (2) The topic of learning from few samples is presented as completely new. It is well known that for classical linear algorithms like the Perceptron and SVM, the weights are a weighted sum of (label-weighted) samples, hence by definition of these algorithm, there is a single sample that can be used to "train" the model in one step. I*d expect some discussion of how the proposed approach relate to these classical approaches. There is also existing literature on a related problem of selecting samples (Teaching dimension Goldman&Kearns) that could be somewhat relevant here. (3) Motivation. The paper provide several motivations for dataset distillation. I support the first motivation of scientific understanding what data is actually needed for a classifier, and this means that deeper analysis is needed. The practical motivations are less convincing, because (a) domain adaptation experiments are not compared with real baselines (b) robustness of poisoning with a single sample is not studied/discussed. (4) experiments: The intro states that training with 10 images reaches 94% accuracy, but this does not seem consistent with the results in Table 1. The caption of figure 2 suggests that accuracy is between 12% and 94% which means the stated 94% is not representative or typical. Could you clarify? For domain adaptation. The baseline (random images) are very weak, and still perform almost comparably to the proposed approach. More robust experiments are needed here: stronger baselines, decent hyper-parameter search etc. (5) Writing and exposition: The paper addresses two issues: (a) learning with few synthetic samples, and (b) learning with few gradient steps. The intro tends to mix the two, and it is not clear why learning with a single gradient step is important. I recommend to separate the two topics more clearly.