ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Official model documentation covering evaluation, limitations and safety considerations. The date records the listed model-card update.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Read a model card as a disclosure document, not a peer-reviewed architectural paper.
@misc{deepmind2026gemini,
title = {Gemini 4 Argon — Model Card},
author = {Google DeepMind},
year = {2026},
url = {https://deepmind.google/models/model-cards/gemini-4-argon/}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.
Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.