idiap / attention-samplingLinks
This Python package enables the training and inference of deep learning models for very large data, such as megapixel images, using attention-sampling
☆97Updated 5 years ago
Alternatives and similar repositories for attention-sampling
Users that are interested in attention-sampling are comparing it to the libraries listed below
Sorting:
- Pytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/180…☆153Updated last year
- This is a PyTorch implementation of the paper: "Processing Megapixel Images with Deep Attention-Sampling Models".☆41Updated last year
- Code for paper Unsupervised Object Segmentation by Redrawing☆177Updated 5 years ago
- ☆93Updated 4 years ago
- An intriguing failing of convolutional neural networks and the CoordConv solution in PyTorch☆72Updated 7 years ago
- Framework for creating (partially) reversible neural networks with PyTorch☆152Updated 3 years ago
- Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands☆111Updated 5 years ago
- ☆182Updated 2 years ago
- Equivariant Transformer (ET) layers are image-to-image mappings that incorporate prior knowledge on invariances with respect to continuo…☆92Updated 6 years ago
- Pytorch implementation of Learning Rate Dropout.☆42Updated 5 years ago
- Numerical Computation of Receptive Field in Pytorch☆62Updated 6 years ago
- Code for Scalable Uncertainty for Computer Vision with Functional Variational Inference @ CVPR 2020☆68Updated 5 years ago
- ☆54Updated 3 years ago
- Differentiable Data Augmentation Library☆123Updated 3 years ago
- Authors official implementation of "Big GANs Are Watching You" pre-print☆114Updated last year
- RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D …☆84Updated 5 years ago
- Code for reproducing experiments in "How Useful is Self-Supervised Pretraining for Visual Tasks?"☆60Updated last year
- [NeurIPS 2020 Oral] Is normalization indispensable for training deep neural networks?☆34Updated 3 years ago
- Official PyTorch implementation of Deformable Grid (ECCV 2020)☆158Updated 4 years ago
- Deep Isometric Learning for Visual Recognition (ICML 2020)☆143Updated 3 years ago
- CPAB Transformations: finite-dimensional spaces of simple, fast, and highly-expressive diffeomorphisms derived from parametric, continuou…☆50Updated 4 years ago
- Implementation of the paper Self-Supervised Learning of Pretext-Invariant Representations☆89Updated 4 years ago
- To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory avail…☆100Updated last year
- Pre-trained NFNets with 99% of the accuracy of the official paper "High-Performance Large-Scale Image Recognition Without Normalization".☆159Updated 4 years ago
- SCOPS: Self-Supervised Co-Part Segmentation (CVPR'19)☆218Updated 2 years ago
- Implements the unsupervised pre-training of convolutional neural networks☆252Updated 3 years ago
- Code for "Are labels necessary for neural architecture search"☆92Updated last year
- Dense Steerable Filter CNN☆77Updated 2 years ago
- Minimal API for receptive field calculation in PyTorch☆68Updated 2 years ago
- ☆71Updated 6 years ago