What is the computational overhead of layer-wise score aggregation method compared to last-layer-only baseline

Abstract

Abstract: The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques

Research Question

What is the computational overhead of layer-wise score aggregation method compared to last-layer-only baselines on SuperGLUE benchmark tasks?

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