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However, if not pre-trained, the transformer architecture doesn\u2019t work well on small datasets and is surpassed by CNN. Through analysis, we found that:(1) the division and processing of tokens in the ViT discard the marginalized information between token. (2) the isolated multi-head self-attention (MSA) lacks prior knowledge. (3) the local inductive bias capability of stacked transformer block is much inferior to that of CNN. We propose a novel architecture for small data paradigms without pre-training, named Add-Vit, which uses progressive tokenization with feature supplementation in patch embedding. The model\u2019s representational ability is enhanced by using a convolutional prediction module shortcut to connect MSA and capture local features as additional representations of the token. Without the need for pre-training on large datasets, our best model achieved 81.25<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> accuracy when trained from scratch on the CIFAR-100.\n<\/jats:p>","DOI":"10.1007\/s11063-024-11643-8","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T20:07:12Z","timestamp":1717790832000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Add-Vit: CNN-Transformer Hybrid Architecture for Small Data Paradigm Processing"],"prefix":"10.1007","volume":"56","author":[{"given":"Jinhui","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"11643_CR1","doi-asserted-by":"publisher","unstructured":"Chen M, Radford A, Child R, Wu J, Jun H, Luan D, Sutskever I (2020) Generative pretraining from pixels. 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The code of the proposed method available on request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}],"article-number":"198"}}