Combined Scaling for Zero-shot Transfer Learning

Understand

We present a combined scaling method - named BASIC - that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet example.

• This accuracy surpasses best published similar models - CLIP and ALIGN - by 9.3%.
• Our BASIC model also shows significant improvements in robustness benchmarks.
• For instance, on 5 test sets with natural distribution shifts such as ImageNet-{A,R,V2,Sketch} and ObjectNet, our model achieves 84.3% top-1 average accuracy, only a small drop from its original ImageNet accuracy.