A Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion
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Updated
Jun 5, 2024 - Python
A Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion
A simple PyTorch implementation of conditional denoising diffusion probabilistic models (DDPM) on MNIST, Fashion-MNIST, and Sprite datasets
Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets
Introduction to neural networks - from scratch.
This code tackles the classic Fashion MNIST image classification task using TensorFlow and a multi-layer perceptron (MLP) neural network.
Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)
Fashion MNIST linear classification model with Keras 3.0 and PyTorch
LightLayers: Parameter Efficient Dense and convolutional Layers for Image Classification (PDCAT-PAAP)
Clothing detection dataset
Simple fashion accessories recognizing AI
WRN 40-4 training from scratch. Best test accuracy on Fashion MNIST dataset is ~96.74%; best test accuracy on Cifar-10 dataset is ~98.03%.
Course project of SJTU CS3612: Machine Learning, 2023 spring
This is a playground to experience and test new frontiers in Artificial Intelligence
Image classification on fashion_mnist
This repository contains the files for the first assignment of the course CS6910 - Deep Learning at IIT Madras.
Experiments for understanding disentanglement in VAE latent representations
Two white-box evasion attacks– FGSM + PGD– on a LeNet-5 model trained on Fashion MNIST
PyTorch implementation of deep CNNs
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