Using CLIP to Classify Images without any Labels

Deep image classification models are typically trained in a supervised manner over a large, annotated dataset. Although a model's performance will improve as more annotated data becomes available, large-scale datasets for supervised learning are often difficult and expensive to obtain, requiring numerous hours of effort from expert annotators. With this in mind, one may begin to wonder if cheaper sources of supervision exist. Put simply, is it possible learn high-quality image classification models from data this is already publicly available? The proposal of Contrastive Language-Image Pre-Training (CLIP) model [1] -- recently re-popularized due to its use in the DALLE-2 model--by OpenAI answered this question in a positive fashion.