Inspired by the existing analysis for contrastive learning, a reproducing kernel Hilbert space (RKHS) understanding of many existing non-contrastive ...
May 27, 2023 · We introduce Matrix-SSL, a novel approach that leverages matrix information theory to interpret the maximum entropy encoding loss as matrix uniformity loss.
In experiments, our method Kernel-SSL outperforms state-of-the-art methods by a large margin on ImageNet datasets under the linear evaluation settings.
In experiments, our method Kernel-SSL outperforms state-of-the-art methods by a large margin on ImageNet datasets under the linear evaluation settings.
May 27, 2023 · We introduce Matrix-SSL, a novel approach that leverages matrix information theory to interpret the maximum entropy encoding loss as matrix uniformity loss.
To bridge this gap, we introduce matrix information theory. By extending classical concepts like en- tropy, Kullback–Leibler (KL) divergence, and cross-entropy.
Apr 7, 2011 · A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications. The author suggests using the kernel function for two ...
Missing: Self- | Show results with:Self-
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks.
Jun 29, 2024 · Self-supervised learning is a machine learning approach that enables models to learn from unlabelled data, revolutionizing how we approach ...
Self-supervised learning with rotation-invariant kernels · Kernel-SSL: Kernel KL Divergence for Self-Supervised Learning · Self-Supervised Learning with an ...