We revisit a simple Learning-from-Scratch baseline for visuo-motor control that uses data augmentation and a shallow ConvNet. We find that this baseline has competitive performance with recent methods that leverage frozen visual representations trained on large-scale vision datasets.
翻译:我们重新审视了使用数据扩增和浅度ConvNet的相对机体控制简单的“从边上学习”基线。 我们发现,这一基线具有竞争性性能,而最近采用的方法是利用在大规模视觉数据集方面受过培训的冷冻视觉显示器。