Initial commit

.gitignore

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+# Byte-compiled / optimized / DLL files
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+
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+#.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2023 Aleksandr Karakulev
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# RLVI: Robust Learning via Variational Inference
+
+Implementation of [Adaptive Robust Learning using Latent Bernoulli Variables](https://arxiv.org/pdf/2312.00585).
+
+Our method, called RLVI, enables robust maximization of the likelihood. The learned parametric model is thus accurate even when the training data is corrupted. Additionally, RLVI is well-suited for online or stochastic optimization as it does not require estimating the total ratio of contaminated data and adaptively infers the probabilities of sample corruption.  
+
+
+## Standard parameter estimation
+Benchmark, reproduced from [(Osama et al., 2020)](https://doi.org/10.1109/OJSP.2020.3039632), that compares robust learning algorithms on the four test problems with corrupted samples in the training data: linear and logistic regression, principal component analysis, covariance estimation.
+```
+cd standard-learning
+python3 main.py
+```
+
+## Online learning
+Binary classification for Human Activity Recognition dataset [(Amine El Helou, 2023)](https://www.mathworks.com/matlabcentral/fileexchange/54138-sensor-har-recognition-app), performed in batches with varying level of corruption to simulate the online learning setting.
+```
+cd online-learning
+python3 main.py
+```
+
+Accuracy levels are higher with RLVI than with stochastic likelihood maximization (SGD).
+<p align="center">
+  <img src="img/online-learning.png" alt="Online learning" width="300"/>  
+</p>
+
+## Deep learning
+### Synthetic noise (MNIST, CIFAR10, CIFAR100)
+Experiments with the datasets in which training data is corrupted with synthetic noise.
+There are four types of noise: `symmetric`, `asymmetric`, `pairflip`, and `instance`. Noise rate from 0 to 1 needs to be specified to corrupt the training set.
+For the method, one can use `rlvi` or one of the following: `regular`, `coteaching` [(Han et al., 2018)](https://papers.nips.cc/paper_files/paper/2018/hash/a19744e268754fb0148b017647355b7b-Abstract.html), `jocor` [(Wei et al., 2020)](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wei_Combating_Noisy_Labels_by_Agreement_A_Joint_Training_Method_with_CVPR_2020_paper.pdf), `cdr` [(Xia et al., 2020)](https://openreview.net/forum?id=Eql5b1_hTE4), `usdnl` [(Xu et al., 2023)](https://doi.org/10.1609/aaai.v37i9.26264), and `bare` [(Patel & Sastry, 2023)](https://openaccess.thecvf.com/content/WACV2023/papers/Patel_Adaptive_Sample_Selection_for_Robust_Learning_Under_Label_Noise_WACV_2023_paper.pdf).
+
+Example:
+```
+cd deep-learning
+python3 main.py \
+        --method=rlvi \
+        --dataset=mnist \
+        --noise_type=pairflip \
+        --noise_rate=0.45
+```
+
+<p align="center">
+  <img src="img/deep-learning-synthetic.png" alt="Deep learning synthetic" width="300"/>  
+</p>
+
+### Real noise (Food101)
+Experiments with the dataset in which training data is corrupted by nature: some of the training images are mislabeled and contain some noise.
+For the method, one can specify `rlvi` or one of the following: `regular`, `coteaching`, `jocor`, `cdr`, `usdnl`, `bare`.
+
+Example:
+```
+cd deep-learning
+python3 food.py --method=rlvi
+```