This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach that is, in theory, applicable to supervised, unsupervised, and reinforcement learning, although our experiments only focus on the supervised case. GTNs are deep neural networks that generate data and/or training environments that a learner (e.g. a freshly initialized neural network) trains on for a few SGD steps before being tested on a target task. We then differentiate through the entire learning process via meta-gradients to update the GTN parameters to improve performance on the target task. GTNs have the beneficial property that they can theoretically generate any type of data or training environment, making their potential impact large. This paper introduces GTNs, discusses their potential, and showcases that they can substantially accelerate learning. We also demonstrate a practical and exciting application of GTNs: accelerating the evaluation of candidate architectures for neural architecture search (NAS), which is rate-limited by such evaluations, enabling massive speed-ups in NAS. GTN-NAS improves the NAS state of the art, finding higher performing architectures when controlling for the search proposal mechanism. GTN-NAS also is competitive with the overall state of the art approaches, which achieve top performance while using orders of magnitude less computation than typical NAS methods. Speculating forward, GTNs may represent a first step toward the ambitious goal of algorithms that generate their own training data and, in doing so, open a variety of interesting new research questions and directions.
This paper proposes a meta-learning algorithm Generative Teaching Networks (GTN) to generate fake training data for models to learn more accurate models. In the inner loop, a generator produces training data and the learner takes gradient steps on this data. In the outer loop, the parameters of the generator are updated by evaluating the learner on real data and differentiating through the gradient steps of the inner loop. The main claim is this method is shown to give improvements in performance on supervised learning for MNIST and CIFAR10. They also suggest weight normalization patches up instability issues with meta-learning and evaluate this in the supervised learning setting, and curriculum learning for GTNs.
To me, the main claim is very surprising and counter-intuitive - it is not clear where the extra juice is coming from, as the algorithm does not assume any extra information. The actual results I believe do not bear out this claim because the actual results on MNIST and CIFAR10 are significantly below state of the art. On MNIST, GTN achieves about 98% accuracy and the baseline “Real Data” achieves  <97% accuracy, while the state of the art is about 99.7% and well-tuned convnets without any pre-processing or fancy extras achieve about 99% according to Yann LeCunn’s website. The disparity on CIFAR seems to be less egregious but the state of the art stands at 99% while the best GTN model (without cutout) achieves about 96.2% which matches good convnets and is slightly worse than neural architecture search according to https://paperswithcode.com/sota/image-classification-on-cifar-10.
This does not negate the potential of GTNs which I feel are an interesting approach, but I believe the paper should be more straightforward with the presentation of these results. The current results basically  show that GTNs improve the performance of learners with bad hyper-parameters. On problems that are not as well-studied as MNIST or CIFAR10 this could still be very valuable (as we do not know what performance is good or bad in advance). Based on the results, GTN does seem to be a significant step forward in synthetic data generation for learning compared to prior work (Zhang 2018, Luo 2018).
The paper proposes two other contributions: using weight normalization for meta-learning and curriculum learning for GTNs. Weight normalization is shown to stabilize GTNs on MNIST. I think the paper oversteps in the relevant method section, hypothesizing it may stabilize meta-learning more broadly. The paper should present a wider set of experiments to make this claim convincing. But the point for GTNs on MNIST nevertheless stands. For curriculum learning: the description of the method is done across section 2 and section 3.2 and does not really describe it completely. How exactly are the samples chosen in GTN - All Shuffled? How does GTN - Full Curriculum and Shuffled Batch parametrize the order of the samples so that it can be learned? I suggest that this information is all included as a subsection in the method (section 2). The results seem to show the learned curriculum is superior to no curriculum.
At a high level it would be very surprising to me if the way forward for better discriminative models was to learn good generative models and use them again for training discriminative models, simply because discriminative models have proved thus far significantly easier to train. If this work does eventually show this result, it would be a very interesting result. At the moment, I believe it does not, but I would be happy to change my mind if the authors provide convincing evidence. Alternatively, I feel that the paper could be a valuable contribution to the community if the writing is toned down to focus on the contributions, presents the results comparing to well-tuned hyperparameters and not over-claim.
More comments:
What is the outer loop loss function? Is it assumed to be the same as the inner one (but using real data instead of training data)? I think this should be made explicit in the method section.
There are some additional experiments in other settings such as RL and unsupervised learning. Both seem like quite interesting directions but seem like preliminary experiments that don’t work convincingly yet. The RL experiment shows that using GTN does not change performance much. There is a claim about optimizing randomly initialized networks at each step, but the baseline which uses randomly initialized networks at each step with A2C is missing. The GAN experiments shows the GAN loss makes GTN realistic (as expected) but there are no quantitative results on mode collapse. (Another interesting experiment would be to show how adding a GAN loss for generating data affects the test performance of the method.) Perhaps it would benefit the paper to narrow in on supervised learning? Given that these final experiments are not polished, the claim in the abstract that the method is “a general approach that is applicable to supervised, unsupervised, and reinforcement learning” seems to be over-claiming. I understand it can be applicable but the paper has not really done the work to show this outside the supervised learning setting.
Minor comments:
Pg. 4: comperable -> comparable