  We introduce CAN, a simple, efficient and scalable method for self-supervised
learning of visual representations. Our framework is a minimal and conceptually
clean synthesis of (C) contrastive learning, (A) masked autoencoders, and (N)
the noise prediction approach used in diffusion models. The learning mechanisms
are complementary to one another: contrastive learning shapes the embedding
space across a batch of image samples; masked autoencoders focus on
reconstruction of the low-frequency spatial correlations in a single image
sample; and noise prediction encourages the reconstruction of the
high-frequency components of an image. The combined approach results in a
robust, scalable and simple-to-implement algorithm. The training process is
symmetric, with 50% of patches in both views being masked at random, yielding a
considerable efficiency improvement over prior contrastive learning methods.
Extensive empirical studies demonstrate that CAN achieves strong downstream
performance under both linear and finetuning evaluations on transfer learning
and robustness tasks. CAN outperforms MAE and SimCLR when pre-training on
ImageNet, but is especially useful for pre-training on larger uncurated
datasets such as JFT-300M: for linear probe on ImageNet, CAN achieves 75.4%
compared to 73.4% for SimCLR and 64.1% for MAE. The finetuned performance on
ImageNet of our ViT-L model is 86.1%, compared to 85.5% for SimCLR, and 85.4%
for MAE. The overall FLOPs load of SimCLR is 70% higher than CAN for ViT-L
models.
