Jun 17, 2022 · TKIL approach is based on Neural Tangent Kernel (NTK), which describes the convergence behavior of neural networks as a kernel function in the ...
Specifically, TKIL achieves such equilibrium by tuning different task-specific parameters for different tasks with a new Gradient Tangent Kernel (GTK) loss.
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We propose a novel class incremental learning approach,. TKIL, that addresses the imbalances in memory-based in- cremental learning. 2) The core of TKIL is a ...
Nov 8, 2022 · This paper proposes to tackle the problem of data imbalance in class incremental learning. The originality of the approach is to use Neural Tangent Kernel (NTK) ...
In our work, we propose to address these challenges with the introduction of a novel methodology of Tangent Kernel for Incremental Learning (TKIL) that achieves ...
We propose a tangent kernel optimization approach for class balanced incremental learning, TKIL, that addresses imbalances in memory-based incremental learning.
The approach preserves the representations across classes and balances the accuracy for each class, and as such achieves better overall accuracy and variance.
TKIL is a Tangent Kernel optimization for optimal class-balanced Incremental Learning ... Tangent Kernel Incremental Learning (TKIL) Approach: Visualizations.
... This approach aims to refine the experiment output to align more closely with the pre-designed electronic network. To prevent overfitting, we strategically ...
Tkil: Tangent kernel optimization for class balanced incremental learning. J Xiang, E Shlizerman. Proceedings of the IEEE/CVF International Conference on ...