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Found 4 papers, 2 papers with code
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77. 1% and various types of natural variations in the images at inference.
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field.
Growing a Brain with Sparsity-Inducing Generation for Continual Learning
The base network learns knowledge of sequential tasks, and the sparsity-inducing hypernetwork generates parameters for each time step for evolving old knowledge.
Helpful or Harmful: Inter-Task Association in Continual Learning
In this work, we propose a novel approach to differentiate helpful and harmful information for old tasks using a model search to learn a current task effectively.
Ranked #1 on
Continual Learning
on Split MNIST (5 tasks)
