yyyvvs's starred repositories
monodepth360
Master's project implementing depth estimation for spherical images using unsupervised learning with CNNs.
picture-clustering
Image clustering with unsupervised learning using CNN
Unsupervised-Features-Learning-for-Image-Classification
Recently, image classification draw attentions of many researchers. The need of object recognition grows drastically, especially in the context of biometric, biomedical imaging and real time scene understanding. Computer vision task is the most challenging in machine learning. For that reason, it's fundamental to tackle this concern using appropriate clustering and classification techniques. However, the quest for the best unsupervised features extraction remain an open problem even if CNNs reach a remarkable success, establishing new state-of-the-art. In this context, we study from an acute insight standpoint the standard clustering models K-means, GMM and Naive Bayes classification algorithm in order to draw conclusion and underline their limits for such complicated tasks. To what extent are k-means and GMM efficient ? Why they fail and how to circumvent their weaknesses.
The_Linnaeus_Bot
In this project, I'm using TensorFlow/Keras to develop a deep learning CNN to classify images of damselflies and dragonflies, reconstructing images of dragonflies.
Deep-Learning-modules-in-Keras
A project about the brief implementation of MLP, CNN, RNN, Unsupervised learning with Autoencoders, ETC, Text analytics using CNN in Python
Convolutional-Neural-Network-and-Autoencoder-Parallel
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery. An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction.