[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

A PyTorch implementation of Mnasnet: MnasNet: Platform-Aware Neural Architecture Search for Mobile.

DeepArchitect: Automatically Designing and Training Deep Architectures

A general, modular, and programmable architecture search framework

A Tensorflow Keras implementation (Graph and eager execution) of Mnasnet: MnasNet: Platform-Aware Neural Architecture Search for Mobile.

Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support.

A Fast DNN Accelerator Design Space Exploration Framework.

This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"

A neural architecture optimizer, targeted at model-size w.r.t. accuracy

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Classification

Implementation of Autoslim using Tensorflow2

Distributed Network Architecture Search

A TensorFlow 2.0 implementation of MnasNet: Platform-Aware Neural Architecture Search for Mobile.

Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Generation with Asymmetric Variational Autoencoders

Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Generation with GAN where the Discriminator network is fixed and same as that in the infoGAN paper (https://arxiv.org/abs/1606.03657)

Code for ''Understanding and Exploring the Network with Stochastic Architectures''

A design space exploration tool for deep neural architectures

Architecture search using unsupervised learning with symmetric auto-encoders and QLearning in PyTorch