Pytorch implementation of Center Loss
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Updated
Feb 19, 2023 - Python
Pytorch implementation of Center Loss
Experiments on unsupervised point cloud reconstruction.
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
A simple Tensorflow based library for deep and/or denoising AutoEncoder.
Leveraging Inlier Correspondences Proportion for Point Cloud Registration. https://arxiv.org/abs/2201.12094.
OhmNet: Representation learning in multi-layer graphs
Temporal-spatial Feature Learning of DCE-MR Images via 3DCNN
Feature learning over RDF data and OWL ontologies
Code for paper "Learning Semantically Enhanced Feature for Fine-grained Image Classification"
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Easy-to-read implementation of self-supervised learning using vision transformer and knowledge distillation with no labels - DINO 😃
A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Experiments on point cloud segmentation.
Self-Supervised Feature Learning by Learning to Spot Artifacts. In CVPR, 2018.
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics. In CVPR, 2020.
This is an implementation of the Center Loss article (2016).
A zero-shot document classifier.
Ensembles and hyperparameter optimization for clustering pipelines.
Code for reproducing the paper "Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning"
Unsupervised feature learning in multi-layer networks
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