rtflynn / Cifar-Autoencoder
A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.
☆64Updated 6 years ago
Alternatives and similar repositories for Cifar-Autoencoder:
Users that are interested in Cifar-Autoencoder are comparing it to the libraries listed below
- This is a reimplementation of the blog post "Building Autoencoders in Keras". Instead of using MNIST, this project uses CIFAR10.☆70Updated 6 years ago
- This is an implementation of the VAE (Variational Autoencoder) for Cifar10☆67Updated 3 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆160Updated 4 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 3 years ago
- Robust Out-of-distribution Detection in Neural Networks☆72Updated 2 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)☆56Updated 4 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- [ICLR'21] Counterfactual Generative Networks☆107Updated 3 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆55Updated 3 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)☆34Updated 4 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆140Updated 4 years ago
- Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples…☆95Updated 2 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆44Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 2 years ago
- Implementation of Wasserstein adversarial attacks.☆23Updated 4 years ago
- PyTorch implementations of Adversarial defenses and utils.☆34Updated last year
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆59Updated 5 years ago
- ☆87Updated 5 months ago
- A way to achieve uniform confidence far away from the training data.☆37Updated 3 years ago
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆23Updated 4 years ago
- Pytorch SimCLR on CIFAR10 (92.85% test accuracy)☆59Updated 4 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆65Updated 5 years ago
- Coresets via Bilevel Optimization☆65Updated 4 years ago
- Code for the paper "Adversarial Self-supervised Contrastive Learning" (NeurIPS 2020)☆171Updated 2 years ago
- Pytorch version of NIPS'16 "Learning to learn by gradient descent by gradient descent"☆62Updated last year