feature-learning

pathak22/unsupervised-video

[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web

antao97/UnsupervisedPointCloudReconstruction

Experiments on unsupervised point cloud reconstruction.

JuanDuGit/DH3D

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

rajarsheem/libsdae-autoencoder-tensorflow

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

zhulf0804/GCNet

Leveraging Inlier Correspondences Proportion for Point Cloud Registration. https://arxiv.org/abs/2201.12094.

getml/getml-community

Fast, high-quality forecasts on relational and multivariate time-series data powered by new feature learning algorithms and automated ML.

mims-harvard/ohmnet

OhmNet: Representation learning in multi-layer graphs

xyj77/MCF-3D-CNN

Temporal-spatial Feature Learning of DCE-MR Images via 3DCNN

bio-ontology-research-group/walking-rdf-and-owl

Feature learning over RDF data and OWL ontologies

yafangshih/Deep-COOC

Deep Co-occurrence Feature Learning for Visual Object Recognition (CVPR 2017)

cswluo/SEF

Code for paper "Learning Semantically Enhanced Feature for Fine-grained Image Classification"

CarsonScott/Competitive-Feature-Learning

Online feature-extraction and classification algorithm that learns representations of input patterns.

LFhase/FeAT

[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization

manhph2211/Self-Supervised-Distillation

Easy-to-read implementation of self-supervised learning using vision transformer and knowledge distillation with no labels - DINO :smiley:

bruAristimunha/Re-Deep-Convolution-Neural-Network-and-Autoencoders-Based-Unsupervised-Feature-Learning-of-EEG

ml-uol/prosper

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

antao97/PointCloudSegmentation

Experiments on point cloud segmentation.

sjenni/LearningToSpotArtifacts

Self-Supervised Feature Learning by Learning to Spot Artifacts. In CVPR, 2018.

dreizehnutters/pcapAE

convGRU based autoencoder for unsupervised & spatial-temporal anomaly detection in computer network (PCAP) traffic.

sjenni/LCI

Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics. In CVPR, 2020.

zoli333/Center-Loss

This is an implementation of the Center Loss article (2016).

rikturr/mml-feature-learning

Miami Machine Learning Meetup - Feature Learning with Matrix Factorization and Neural Networks

GabrielFernandezFernandez/SPIVAE

Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.

hmohebbi/CNN_featureLearning_SVM_classifier

Image Classification via Transfer Learning: Using Pre-trained Densely Connected Convolutional Network (DenseNet) weights

zacrash/world-models-feature-learning

Experiment with World Models by Ha et al. using Variational Recurrent Neural Networks for more task relevant feature learning

eigenvivek/Grad-CAMO

[CVPRW 2024] Learning interpretable single-cell morphological profiles from 3D Cell Painting z-stacks

DocsaidLab/DocClassifier

A zero-shot document classifier.

INSPIRE-Lab-US/LSR-dictionary-learning

Associated codebase for the paper "Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms"

alex-kom/Cluster-HyperEnsembles

Ensembles and hyperparameter optimization for clustering pipelines.

EEE17A/DNN-for-NILM

In this project, we've tried applying various DNNs to the problem of non-intrusive load monitoring (NILM) and compared their results for various appliances using the REDD dataset. We took a sliding window approach in hopes that we'll be able to achieve real time disaggregation with further tuning and testing. We compare the disaggregated energy consumption results based on MSE, MAE, Relative Error and F1 Score.

sedflix/tripletgan.pytorch

Implementation of the paper Training Triplet Networks with GAN

AntonioScl/SGD_learning_regimes

Code for reproducing the paper "Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning"

cocoakang/colmap_multichannel

A modified COLMAP to take as input multi-channel images. It can be used to evaluate the proposed multi-channel feature/descriptor.

lelea2/kdao

Collections of my personal prototypes for works, hackathon and personal project

swastishreya/Feature-Learning

We aim to illustrate the difference between feature extraction and feature learning. We see that when using classical machine learning models, there is a requirement to come up with features (input to the model) “explicitly”, that would give the best and suitable output for the task in hand. However, when using deep learning models, these features are derived “implicitly” by the model as the training progresses.