Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
This work addresses the data efficiency problem for researchers and practitioners in computer vision by demonstrating that large datasets may not be essential, though it is incremental in optimizing pre-training methods.
The paper tackles the problem of whether large-scale datasets like ImageNet are necessary for self-supervised pre-training by showing that denoising autoencoders can achieve competitive performance using only smaller target task data, such as surpassing supervised ImageNet pre-training on COCO for detection and instance segmentation.
Pre-training models on large scale datasets, like ImageNet, is a standard practice in computer vision. This paradigm is especially effective for tasks with small training sets, for which high-capacity models tend to overfit. In this work, we consider a self-supervised pre-training scenario that only leverages the target task data. We consider datasets, like Stanford Cars, Sketch or COCO, which are order(s) of magnitude smaller than Imagenet. Our study shows that denoising autoencoders, such as BEiT or a variant that we introduce in this paper, are more robust to the type and size of the pre-training data than popular self-supervised methods trained by comparing image embeddings.We obtain competitive performance compared to ImageNet pre-training on a variety of classification datasets, from different domains. On COCO, when pre-training solely using COCO images, the detection and instance segmentation performance surpasses the supervised ImageNet pre-training in a comparable setting.