The **Bottleneck Transformer (BoTNet) ** is an image classification model that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a [ResNet](https://paperswithcode.com/method/resnet) and no other changes, the approach improves upon baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency.

**Variational Quantum Singular Value Decomposition** is a variational quantum algorithm for singular value decomposition (VQSVD). By exploiting the variational principles for singular values and the Ky Fan Theorem, a novel loss function is designed such that two quantum neural networks (or parameterized quantum circuits) could be trained to learn the singular vectors and output the corresponding singular values.

VQSVD

Variational Quantum Singular Value Decomposition

Bottleneck Transformer

Bottleneck Transformers for Visual Recognition

**Co-Correcting** is a noise-tolerant deep learning framework for medical image classification based on mutual learning and annotation correction. It consists of three modules: the dual-network architecture, the curriculum learning module, and the label correction module.

Source	Bottleneck Transformers for Visual Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com