Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟰𝟭 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗦𝗲𝗮 𝗔𝗜 𝗟𝗮𝗯 𝗮𝗻𝗱 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗼𝗳 𝗦𝗶𝗻𝗴𝗮𝗽𝗼𝗿𝗲 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 ‘𝗣𝗼𝗼𝗹𝗙𝗼𝗿𝗺𝗲𝗿’: 𝗔 𝗗𝗲𝗿𝗶𝘃𝗲𝗱 𝗠𝗼𝗱𝗲𝗹 𝗳𝗿𝗼𝗺 𝗠𝗲𝘁𝗮𝗙𝗼𝗿𝗺𝗲𝗿 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗧𝗮𝘀𝗸𝘀 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published arxiv2021. 🔸 The main hype of the last few years in the world of Deep Learning is definitely Transformers. Since their advent in 2017 with the super-cited paper Attention Is All You Need, many researchers have struggled to improve and apply them in every possible domain. While originally born for NLP, the interest in Transformers applied to vision is growing exponentially, and, since the introduction of ViT, many research groups have proposed different variants of its architecture. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. 🔹However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. 🔸To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. 🔹For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. 🔸The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. 🔹Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. 🔸This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------