- Fig. 1 leaves me with some doubts. It would seem that the private task is solved by using only a head operating on the learned layer for the green task (devanagari). This is at least what I would expect for the claims of the method to still uphold, because if the private task head can alter the weights of the Transformer layer 1 then information from the private task is flowing into the network. I would appreaciate if the authors could clarify this.
- Overall a lot of choices seem to lead towards the necessity for large compute power. The choice of modifying hyperparameters only by a one-hop neighbor is quite restrictive and it implies that we have to evolve/search for quite a while before stumbling on the correct hyperparams. The layer cloning and mutation probability hyperparameter is set at random by the evolutionary process, implying that the level of overall randomicity is very high and therefore large training times are needed to get stable results or be able to reproduce the claimed results (considering the authors use DNN architectures). The authors mention that "the score can be defined to optimize a mixture of factors depending on application requirements". It would have been nice to see what the tradeoff between training time and model size vs optimal multi-task performance is, especially considering these high levels of randomicity present in the proposed approach. (and also for others to be able to reproduce somewhat similar results on lesser compute).
- The parameters in Table 1, the model and the experiments seem to be only good for image data and ViT. Did the authors try to apply the same principles to other areas research areas such as NLP or simpler models in the image domain (CNNs)? I understand the latter might be due to the focus about state of the art performance, but it would show that the method can generalize to different architectures and tasks, not just transformers in vision.
- The parameters in Table 1, the model and the experiments seem to be only good for image data and ViT. Did the authors try to apply the same principles to other areas research areas such as NLP or simpler models in the image domain (CNNs)? I understand the latter might be due to the focus about state of the art performance, but it would show that the method can generalize to different architectures and tasks, not just transformers in vision.