1 citations · 1 across the 13 of their papers we have counts for
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AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
Run He, Kai Tong, Di Fang +5
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) with pre-…
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
Huiping Zhuang, Run He, Kai Tong +3
Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic for…
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning
Huiping Zhuang, Yuchen Liu, Run He +5
Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is C…
GACL: Exemplar-Free Generalized Analytic Continual Learning
Huiping Zhuang, Yizhu Chen, Di Fang +5
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose pre…
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Run He, Di Fang, Yizhu Chen +5
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemp…