Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation

**Paper**: [https://arxiv.org/abs/2311.08525](https://arxiv.org/abs/2311.08525)

**Code**: [https://github.com/tuggeluk/ffcv-imagenet/tree/rotation\_module](https://github.com/tuggeluk/ffcv-imagenet/tree/rotation_module)

**Abstract**:

>Humans and animals recognize objects irrespective of the beholder's point of view, which may drastically change their appearances. Artificial pattern recognizers also strive to achieve this, e.g., through translational invariance in convolutional neural networks (CNNs). However, both CNNs and vision transformers (ViTs) perform very poorly on rotated inputs. Here we present artificial mental rotation (AMR), a novel deep learning paradigm for dealing with in-plane rotations inspired by the neuro-psychological concept of mental rotation. Our simple …

abstract amr animals artificial change cnns computervision convolutional neural networks deep learning humans networks neural networks novel objects rotation through transformers vision vision transformers