From a pure technical point of view, I agree with you. But there is no doubt they all have played an important role in popularising deeplearning. I am fancisnated in the history of deeplearning and how it went from a field no one cared to what it is today.
IMHO the ImageNet 2012 competition and the winning solution AlexNet (Krizhevsky et al.) was the pivotal moment in which (deep) neural nets went from a field only a few wizened academics cared about to becoming today's buzz word.
It did 3 things:
1. Provided a usable solution for what was previously an intractable real world problem, large multiclass image
classification, with decent accuracy
2. Crushed the prior benchmark on this task
3. Found a practical workaround to what was the biggest bottleneck, computation time, by utilizing GPUs (and made Nvidia stock explode /s)
The subsequent ImageNet competitions then later provided the perfect catalyst to refining and making deep neural nets mainstream. In parallel the sudden interest from everyone else who in turn started applying neural nets to pretty much every domain out there under the sun, was what I think ultimately made deep learning as it is to what it's today.
