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Found 6 papers, 4 papers with code
Analytic Federated Learning
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i. e., closed-form) solutions to the federated learning (FL) community.
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
The compensation stream is governed by a Dual-Activation Compensation (DAC) module.
AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low Time and Resource Consumption
Online Class Incremental Learning (OCIL) aims to train the model in a task-by-task manner, where data arrive in mini-batches at a time while previous data are not accessible.
G-ACIL: Analytic Learning for Exemplar-Free Generalized Class Incremental Learning
The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution, leading to intensified forgetting.
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
The DS-BPT pretrains model in streams of both supervised learning and self-supervised contrastive learning (SSCL) for base knowledge extraction.
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems.
