https://developer.nvidia.com/blog/flip-a-difference-evaluator-for-alternating-images/

**DistilBERT**  is a small, fast, cheap and light [Transformer](https://paperswithcode.com/method/transformer) model based on the [BERT](https://paperswithcode.com/method/bert) architecture. Knowledge distillation is performed during the pre-training phase to reduce the size of a BERT model by 40%. To leverage the inductive biases learned by larger models during pre-training, the authors introduce a triple loss combining language modeling, distillation and cosine-distance losses.

DistilBERT

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

FLIP

FLIP: A Difference Evaluator for Alternating Images

**RFB Net** is a one-stage object detector that utilises a receptive field block module. It utilises a VGG16 backbone, and is otherwise quite similar to the [SSD](https://paperswithcode.com/method/ssd) architecture.

Source	FLIP: A Difference Evaluator for Alternating Images
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com