Unsupervised feature learning in multi-layer networks
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Updated
Apr 14, 2017 - Python
Unsupervised feature learning in multi-layer networks
Code for reproducing the paper "Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning"
Ensembles and hyperparameter optimization for clustering pipelines.
A zero-shot document classifier.
This is an implementation of the Center Loss article (2016).
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics. In CVPR, 2020.
Self-Supervised Feature Learning by Learning to Spot Artifacts. In CVPR, 2018.
Experiments on point cloud segmentation.
A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Easy-to-read implementation of self-supervised learning using vision transformer and knowledge distillation with no labels - DINO 😃
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Code for paper "Learning Semantically Enhanced Feature for Fine-grained Image Classification"
Feature learning over RDF data and OWL ontologies
Temporal-spatial Feature Learning of DCE-MR Images via 3DCNN
OhmNet: Representation learning in multi-layer graphs
Leveraging Inlier Correspondences Proportion for Point Cloud Registration. https://arxiv.org/abs/2201.12094.
A simple Tensorflow based library for deep and/or denoising AutoEncoder.
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
Experiments on unsupervised point cloud reconstruction.
Pytorch implementation of Center Loss
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