Stars
[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions, surpassing MobileNetV2
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images
[ECCV 2020] PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
A plug-in replacement for DataLoader to load Imagenet disk-sequentially in PyTorch.
MetaModule provides extensions of PyTorch Module for meta learning
[CVPR 2020] Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives
Official Implementation of "Random Path Selection for Incremental Learning" paper. NeurIPS 2019
Learning to Self-Train for Semi-Supervised Few-Shot
An optimization framework that adapts the batch size in the process of training.
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"
A PyTorch Library for Meta-learning Research
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem [CVPR 2019, oral]
Code for Unsupervised Learning via Meta-Learning.
Source code accompanying our CVPR 2019 paper: "NetTailor: Tuning the architecture, not just the weights."
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
A dataset of datasets for learning to learn from few examples
Implementation of Meta-RL A3C algorithm
An open source python library for scalable Bayesian optimisation.
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Countering Adversarial Image using Input Transformations.
Code for the paper "Generative Adversarial Imitation Learning"
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
DeepArchitect: Automatically Designing and Training Deep Architectures
Code release for paper "Random Search and Reproducibility for NAS"
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog