Using fixed classifiers derived from regular polytopes to enhance neural network efficiency and accuracy by generating stationary, maximally-separated feature representations. read more of Regular Polytope Networks

Elastic Feature Consolidation (EFC), exploits a tractable second-order approximation of feature drift based on an Empirical Feature Matrix (EFM). read more of Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning

We propose a more realistic, physics-based color data augmentation - which we call Planckian Jitter. read more of Planckian jitter: enhancing the color quality of self-supervised visual representations

We propose a two-stage learning baseline with a learnable weight scaling layer for reducing the bias caused by long-tailed distribution in LT-CIL and which in turn also improves the performance of conventional CIL due to the limited exemplars. read more of Long-Tailed Class Incremental Learning

Neural network based on an end-to-end trainable working memory, which acts as an external storage where information about each agent can be continuously written, updated and recalled read more of Smemo: Social memory for trajectory forecasting

This model detects upscaled 4k videos and identifies the upscaling model used. read more of 4k Video Super-Resolution Detection

MIDI-to-Audio piano synthesizer based on DDSP. read more of DDSP-Piano: Differentiable Piano model for MIDI-to-Audio Performance Synthesis

Implementation of different class-incremental methods read more of Class Incremental Learning Methods for Visual Tasks

This recurrent neural network model exploits historical data measured from machine sensors to perform inference on future usage and detect possible future faults in the machine itself. Explainability metrics targets sensor groups and are powered by the SH... read more of GRU + SHAP - Explainable Predictive Maintenance for Irregular Multivariate Time Series

This regression model exploits historical data measured from machine sensors to perform inference on future usage and detect possible future faults in the machine itself. Explainability metrics targets sensor groups and are powered by the SHAP library. read more of Facebook Prophet + SHAP - Explainable Predictive Maintenance for Regular Multivariate Time Series