Introducing utility-based Stochastic Gradient Descent with Adaptive Noise Injection

Deep Learning Project

This is the repository for the ETH Deep Learning project, based on the original paper "Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning" from here. We propose Utility-based Stochastic Gradient Descent with Adaptive Noise injection as an improvement to the Utility-based Perturbed Gradient Descent (UPGD) method. Experiments show that our model achieves higher average accuracy (55.38% vs. 55.29%) and average plasticity (45.52% vs. 41.86%).

Details of our approach and results can be found in our Report

Here we describe how to reproduce the results.

Installation

1. You need to have an environemnt with python 3.7

git clone  --recursive [email protected]:yumikim381/upgd-dl-project.git
python3.7 -m venv .upgd
source .upgd/bin/activate

2. Install Dependencies

python -m pip install --upgrade pip
pip install -r requirements.txt 
pip install HesScale/.
pip install .

Run our Results

1. Run Baseline

We have used Algorithm 1 of the original UPGD as our baseline. This code can be run as follows:

python3 core/run/run_stats.py \
  --task label_permuted_cifar10_stats \
  --learner baseline \
  --seed 19 \
  --lr 0.01 \
  --beta_utility 0.999 \
  --sigma 0.001 \
  --weight_decay 0.0 \
  --network convolutional_network_relu_with_hooks \
  --n_samples 1000000

2. Run Visualizations

Use the notebook notebooks/visualize_kernels.ipynb to run through the visualizations. All have individual cells, and should run with the same set of requirements as the rest of the code, with the potential exception of ipython and jupyterlab.

3. Run best Method with Adaptive Noise Injection

To conduct the accuracy and catastrophic forgetting experiment using the CIFAR-10 dataset, run the following:

python3 core/run/run_stats.py \
  --task label_permuted_cifar10_stats \
  --learner usgd \
  --seed 19 \
  --lr 0.01 \
  --beta_utility 0.999 \
  --sigma 0.001 \
  --weight_decay 0.0 \
  --network convolutional_network_relu_with_hooks \
  --n_samples 1000000

To conduct the loss of plasticity experiment based on the MNIST dataset, run:

python3 core/run/run_stats.py \
  --task input_permuted_mnist_stats \
  --learner usgd \
  --seed 19 \
  --lr 0.01 \
  --beta_utility 0.999 \
  --sigma 0.001 \
  --weight_decay 0.0 \
  --network conv_mnist \
  --n_samples 1000000

usgd can be replaced with the following options to run other variations we have tried out:

1. Layer-wise Noise Scaling
   • weight_norm for scaling by the norm of weights
   • grad_norm for scaling by the norm of gradients
   • ratio_normfor scaling by the ratio of the gradient norm to the weight norm
2. Kernel Utility
   • entire_kernel for entire kernel evaluation
   • column_kernel for column-wise kernel evaluation

4. Get results for all Methods we have implemented

Use the notebook notebooks/get_results.ipynb to get the evaluations of the 2 experiments. The metrics will be printed out in a table format and graphical visualizations of the performance are provided.