Specifically, our model comprises modules of input, autoencoder, transformer encoder, contrastive learning network, and multi-task learning for output. The architecture overview is depicted in Figure ...

The Transformer is an innovative neural network architecture that sweeps away the old assumptions of sequence processing. Instead of linear, step-by-step processing, the Transformer embraces a ...

The autoencoder is optimized to reconstruct this re-masked ... In this study, we evaluate the REMASKER transformer architecture, introduced by Du et al. (2023), by comparing it with traditional ...

We also develop an efficient spike-driven Transformer architecture and a spike-masked autoencoder to prevent performance degradation during SNN scaling. On ImageNet-1k, we achieve state-of-the-art top ...

Masked autoencoder (MAE), which is based on Transformer architecture, employs a “mask-reconstruction” strategy for training, allowing the model to be effective for downstream tasks. However, existing ...

Transformers have a versatile architecture that can be adapted beyond NLP. Transformers have expanded into computer vision through vision transformers (ViTs), which treat patches of images as ...