Paper:
Collaborative Learning for Deep Neural Networks
We introduce collaborative learning in which multiple classifier heads of the same network are simultaneously trained on the same training data to improve generalization and robustness to label noise with no extra inference cost. It acquires the strengths from auxiliary training, multi-task learning and knowledge distillation. There are two important mechanisms involved in collaborative learning. First, the consensus of multiple views from different classifier heads on the same example provides supplementary information as well as regularization to each classifier, thereby improving generalization. Second, intermediate-level representation (ILR) sharing with backpropagation rescaling aggregates the gradient flows from all heads, which not only reduces training computational complexity, but also facilitates supervision to the shared layers. The empirical results on CIFAR and ImageNet datasets demonstrate that deep neural networks learned as a group in a collaborative way significantly reduce the generalization error and increase the robustness to label noise.

Review:
The paper introduces collaborative learning, a modification to architectures that adds additional heads to the classifier in turn enabling better generalization without added inference costs.   Strengths: - The topic of study is of great relevance and significance. While ensembling of networks boosts performance, the added inference cost is typically not warranted; having a way to distill ensemble-like performance in a single model is impactful.  - The authors present results on CIFAR and ImageNet datasets which show how the proposed heuristics improve performance. The ablation experiments are useful and add to the reader's understanding of the changes.   Weaknesses:  - The idea of using heads (or branches) in deep networks is not entirely novel and the authors do not describe or compare against past work such as mixture of experts and mixture of softmax (ICLR 2018). The authors should also investigate the Born Again Networks (ICML 2018) work that uses self-distillation to obtain improved results. It would also be beneficial if the authors described auxiliary training in the related work since they explain their approach using that as foundation (Section 3.1).   - The proposed changes can be explained better. For instance, it is not clear how the hierarchy should be defined for a general classifier and which of the heads is retained during inference.    - What baseline is chosen in Figure 3? Is it also trained on a combination of hard and soft targets?   - The approach presented is quite heuristic it would be desirable if the authors could discuss some theoretical grounding for the proposed ideas. This is especially true for the section on "backpropagation rescaling". Did the authors try using a function that splits the outputs equally for all heads (i.e., I(x) = x/H, I'(x) = 1/H)? The argument for not using sum_h Lh is also not convincing. Why not modulate eta accordingly?  - The paper would benefit from careful editing; there are several typos such as "foucus", "cross entroy", and awkward or colloquial phrases such as "requires to design different classifier heads", "ILR sharing with backpropagation rescaling well aggregates the gradient flow", "confusion often happens to network training", "put on top of individual learning", "SGD has nicer convergence".   Post-rebuttal:  Raising my score to a 6 in response to the rebuttal. I do wish to point out that: - To my understanding, BANs don't ensemble. They only self-distill.  - Is using Lhard the right baseline for Q3?