Multi-Domain Adversarial Learning
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.
 PROS: * Original idea of using separate "discriminator" paths for unknown classes * Thorough theoretical explanation * A variety of experiments * Very well-written, and clear paper CONS: * The biggest problem for me was the unconvincing results. MNIST-to-MNIST-M has better baselines (PixelDA performed better on this task for example), Office is not suitable for domain adaptation experiments anymore unless one wants to be in a few-datasample regime or work with data with noisy labels(the dataset is plagued with label pollution, and there are too few examples per class per domain for NN-based domain adaptation); the results on CELL were not convincing, I don*t know the dataset but it seems that baseline NN does better than DA most of the times. * Comparison with other methods did not take into account a variety of hyperparameters. Although I do understand the problem of evaluation in unsupervised DA, this should have at least been done in the semi-supervised case, and some analysis/discussion should be included for the unsupervised one. What if the proposed method performs that much better than baselines but they hyperparameters are not set correctly?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Multi-Domain Adversarial Learning
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.
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>



 PROS: * Original idea of using separate "discriminator" paths for unknown classes * Thorough theoretical explanation * A variety of experiments * Very well-written, and clear paper CONS: * The biggest problem for me was the unconvincing results. MNIST-to-MNIST-M has better baselines (PixelDA performed better on this task for example), Office is not suitable for domain adaptation experiments anymore unless one wants to be in a few-datasample regime or work with data with noisy labels(the dataset is plagued with label pollution, and there are too few examples per class per domain for NN-based domain adaptation); the results on CELL were not convincing, I don*t know the dataset but it seems that baseline NN does better than DA most of the times. * Comparison with other methods did not take into account a variety of hyperparameters. Although I do understand the problem of evaluation in unsupervised DA, this should have at least been done in the semi-supervised case, and some analysis/discussion should be included for the unsupervised one. What if the proposed method performs that much better than baselines but they hyperparameters are not set correctly?
assistant
 PROS: * Original idea of using separate "discriminator" paths for unknown classes * Thorough theoretical explanation * A variety of experiments * Very well-written, and clear paper CONS: * The biggest problem for me was the unconvincing results. MNIST-to-MNIST-M has better baselines (PixelDA performed better on this task for example), Office is not suitable for domain adaptation experiments anymore unless one wants to be in a few-datasample regime or work with data with noisy labels(the dataset is plagued with label pollution, and there are too few examples per class per domain for NN-based domain adaptation); the results on CELL were not convincing, I don*t know the dataset but it seems that baseline NN does better than DA most of the times. * Comparison with other methods did not take into account a variety of hyperparameters. Although I do understand the problem of evaluation in unsupervised DA, this should have at least been done in the semi-supervised case, and some analysis/discussion should be included for the unsupervised one. What if the proposed method performs that much better than baselines but they hyperparameters are not set correctly?