  Deeper Vision Transformers (ViTs) are more challenging to train. We expose a
degradation problem in deeper layers of ViT when using masked image modeling
(MIM) for pre-training. To ease the training of deeper ViTs, we introduce a
self-supervised learning framework called Masked Image Residual Learning
(MIRL), which significantly alleviates the degradation problem, making scaling
ViT along depth a promising direction for performance upgrade. We reformulate
the pre-training objective for deeper layers of ViT as learning to recover the
residual of the masked image. We provide extensive empirical evidence showing
that deeper ViTs can be effectively optimized using MIRL and easily gain
accuracy from increased depth. With the same level of computational complexity
as ViT-Base and ViT-Large, we instantiate 4.5$\times$ and 2$\times$ deeper
ViTs, dubbed ViT-S-54 and ViT-B-48. The deeper ViT-S-54, costing 3$\times$ less
than ViT-Large, achieves performance on par with ViT-Large. ViT-B-48 achieves
86.2% top-1 accuracy on ImageNet. On one hand, deeper ViTs pre-trained with
MIRL exhibit excellent generalization capabilities on downstream tasks, such as
object detection and semantic segmentation. On the other hand, MIRL
demonstrates high pre-training efficiency. With less pre-training time, MIRL
yields competitive performance compared to other approaches.
