3 accepted papers
Deep neural networks trained on nonstationary data must balance stability (i.e., retaining prior knowledge) and plasticity (i.e., adapting to new tasks). Standard reinitialization methods, which reinitialize weights toward their original values, are widely used but difficult to tune: conservative re…
Esports is growing rapidly, yet the data available to researchers is limited due to the game company policies. Consequently, vision-based approaches utilizing game screens are gaining attention as a practical alternative. We focus on the League of Legends minimap and address the challenges of champi
Plasticity loss, a critical challenge in neural network training, limits a model's ability to adapt to new tasks or shifts in data distribution. While widely used techniques like L2 regularization and Layer Normalization have proven effective in mitigating this issue, Dropout remains notably ineffec…