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One-Shot Learning with Triplet CNNs in Pytorch
Generative Adversarial Networks in TensorFlow 2.0
Vanilla GAN and WGAN implementations in PyTorch on the FashionMNIST dataset
Fashion Mnist image classification using cross entropy and Triplet loss
A pipeline built on MetaFlow for training Fashion MNIST dataset using Pytorch, experiment tracking using MLFlow and model deployment using BentoML
image classification and manipulation in python machine learning on fashion mnist dataset
This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the FashionMNIST dataset. The project aims to generate realistic images of clothing items using a GAN architecture. It includes model definitions, training scripts, and visualizations of generated images at various training stages.
A pytorch implementation of Densenet for FashionMNIST dataset
classification of fashion data(28 x28 greyscale image) into 10 classes.
This project uses an Autoencoder for dimension reduction on the Fashion MNIST dataset, which contains grayscale clothing images. The goal is to reduce the 784-dimensional images (28x28) to a 128-dimensional latent space while reconstructing the images. The performance is evaluated using the Structural Similarity Index (SSIM).
SLIIT 4th Year 2nd Semester Machine Learning Project
This project explores the use of a Generative Adversarial Network (GAN) to generate fashion images from the Fashion MNIST dataset. The generator creates fake images, and the discriminator distinguishes them from real ones. Performance is evaluated using Fréchet Inception Distance (FID) to assess the quality of the generated images.
Deep Learning Project on Diffusion Models for Image Generation
A consortium of popular ML algorithms/concepts implemented in Python.
Une série de notebooks qui expliquent en détail comment fonctionnent les modèles de diffusion
Fashion Image CNN Classifier using Keras
ML project for Content Based Image Recognition using Keras
This repo contains the PyTorch implementation of the paper 'Adversarial Training for Free!', which can be found here: https://arxiv.org/pdf/1904.12843.pdf
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