Search Results for author: Quentin Jodelet

Found 5 papers, 1 papers with code

Class-Incremental Learning using Diffusion Model for Distillation and Replay

no code implementations30 Jun 2023 Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata

Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.

Class Incremental Learning Incremental Learning

Natural Image Reconstruction from fMRI using Deep Learning: A Survey

no code implementations journal 2021 Zarina Rakhimberdina, Quentin Jodelet, Xin Liu, Tsuyoshi Murata

With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain.

Brain Decoding Image Reconstruction

Balanced softmax cross-entropy for incremental learning with and without memory

no code implementations23 Mar 2021 Quentin Jodelet, Xin Liu, Tsuyoshi Murata

When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones.

Class Incremental Learning Incremental Learning +1

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

1 code implementation14 Sep 2020 Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.

Benchmarking Continual Learning

Transfer Learning with Sparse Associative Memories

no code implementations4 Apr 2019 Quentin Jodelet, Vincent Gripon, Masafumi Hagiwara

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification.

General Classification Incremental Learning +1

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