TL;DR
This work proposes a novel Historical Collaborative Learning (HCL) framework empowered by adaptive dual-state historical supervision that jointly exploits long-term and short-term historical knowledge to produce complementary supervision signals.
A lightweight learnable augmentation framework based on Extreme Learning Machines for self-supervised visual representation learning that improves linear evaluation performance relative to reproduced baselines across most settings and demonstrates that lightweight learnable augmentation can effectively enhance self-sup...
Self-supervised representation-guided generative dataset distillation (SRG) is proposed, a framework that translates the SSL geometry into diffusion guidance and consistently outperforms the evaluated generative baselines across multiple datasets and IPC settings.
This work presents a novel self-supervised architecture centered on graph prototype learning that sets a new state-of-the-art on the ARMM dataset with an accuracy of 95.70%, substantiating the efficacy and transferability of prototype-guided self-supervised learning for skeleton-based action representation.
GenView is presented, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics, and an adaptive view generation method that dynamically adjusts the noise level in sampling to ensure the preservation of essential semantic meani...
TSDC outperformed existing deep clustering frameworks, yielding improved classification accuracy and the most prominent improvement was observed on CIFAR-100, where classification accuracy rose from 54.77 ± 0.07 to 57.58 ± 1.85.
This study introduces a novel memory-augmented self-learning framework that extracts and provides diverse learning sources for adaptive knowledge distillation from the student model itself, resulting in a 2.5-6% increase in accuracy across various benchmark datasets compared to current GNN training and self-distillatio...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
