[D] Fairness of comparison of superiority and efficiency between different neural network architectures

During the last 10 years Deep Learning has made impressive progress in various domains, but here I would like to be concrete and focus on computer vision, and in particular ImageNet as a popular benchmark.

Computer vision models have evolved much from vanilla CNNs consisting of only convolutional layers + activations + pooling to more advanced with skip connections, depthwise separable convolutions, squeeze-excitation blocks, and, recently, vision transformers and derivative models. There are a lot of papers proposing some architecture …

comparison efficiency fairness machinelearning network neural network