SITE5039 / AdaMixUp
☆14Updated 5 years ago
Alternatives and similar repositories for AdaMixUp:
Users that are interested in AdaMixUp are comparing it to the libraries listed below
- Official adversarial mixup resynthesis repository☆35Updated 4 years ago
- Tensorflow implementation of "Meta Dropout: Learning to Perturb Latent Features for Generalization" (ICLR 2020)☆27Updated 4 years ago
- Code for the paper: On Symmetric Losses for Learning from Corrupted Labels☆18Updated 5 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 3 years ago
- Gradients as Features for Deep Representation Learning☆43Updated 4 years ago
- Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder…☆17Updated 4 years ago
- Learning To Stop While Learning To Predict☆33Updated 2 years ago
- Tensorflow implementation of "Learning to Balance: Bayesian Meta-learning for Imbalanced and Out-of-distribution Tasks" (ICLR 2020 oral)☆99Updated 4 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- ☆36Updated 4 years ago
- Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].☆26Updated 6 years ago
- pytorch maml with Multi-GPUs, fast and simplest implementation☆13Updated 4 years ago
- ☆34Updated 3 years ago
- Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."☆99Updated 4 years ago
- ☆34Updated 6 years ago
- Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…☆62Updated 4 years ago
- PyTorch Implementations of Dropout Variants☆87Updated 7 years ago
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 5 years ago
- A study on the following problems: what the memorization problem is in meta-learning; why memorization problem happens; and how we can pr…☆21Updated last year
- Tensorflow implementation of DropMax: Adaptive Variational Softmax (NeurIPS2018)☆19Updated 4 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 5 years ago
- Code for Unsupervised Learning via Meta-Learning.☆65Updated 4 years ago
- ☆64Updated 4 years ago
- ☆13Updated 6 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆85Updated 4 years ago
- Gold Loss Correction☆86Updated 6 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- Compressing Neural Networks using the Variational Information Bottleneck☆64Updated 2 years ago
- Code for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"☆25Updated last year
- Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out☆27Updated 3 years ago