Adaptive Aggregation Networks for Class-Incremental Learning

Adaptive Aggregation Networks for Class-Incremental Learning Yaoyao Liu 1 Bernt Schiele 1 Qianru Sun 2 1 Max Planck Institute for Informatics 2 Singapore Management University Task & Challenge & Contributions Framework & Optimization Steps Experiment Results SMU Classification: Restricted ● Task: Class-Incremental Learning [1] - Different classes arrive in different phases; - At any time, it provides a classiﬁer for the classes observed so far; - The memory is limited. References [1] Rebuffi, Sylvestre-Alvise, et al. "icarl: Incremental classifier and representation learning." CVPR 2017; [2] Hou, Saihui, et al. "Learning a unified classifier incrementally via rebalancing." CVPR 2019. ● Challenge : the Stability-plasticity Dilemma - H igh-plasticity models easily forget old classes; - H igh-stability models are weak to learn new classes. ● Contributions - A novel and generic network architecture called AANets specially designed for tackling the stability-plasticity dilemma in CIL tasks; - A BOP-based formulation and its corresponding solution for end-to-end training of the two types of parameters in AANets; - Extensive experiments on three benchmarks by plugging AANets in four baseline methods. Contributions ● Conceptual Illustrations ● The Architecture of AANets ● Optimization Steps - The formulation of bilevel optimization program (BOP) - Updating Parameters - Denotations : parameters for the stable and plastic blocks; : aggregation weights; : exemplars; : new data. ● Ablation Study ● Comparing w/ SOTA ● Grad-CAM Visualizations

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Transcript of Adaptive Aggregation Networks for Class-Incremental Learning

Page 1: Adaptive Aggregation Networks for Class-Incremental Learning

Adaptive Aggregation Networks for Class-Incremental LearningYaoyao Liu1 Bernt Schiele1 Qianru Sun2

1Max Planck Institute for Informatics 2Singapore Management University

Task & Challenge & Contributions Framework & Optimization Steps Experiment Results

SMU Classification: Restricted

● Task: Class-Incremental Learning[1]

- Different classes arrive in different phases;- At any time, it provides a classifier for the classes observed so far;- The memory is limited.

References[1] Rebuffi, Sylvestre-Alvise, et al. "icarl: Incremental classifier and representation learning." CVPR 2017;[2] Hou, Saihui, et al. "Learning a unified classifier incrementally via rebalancing." CVPR 2019.

● Challenge: the Stability-plasticity Dilemma- High-plasticity models easily forget old classes;- High-stability models are weak to learn new classes.

● Contributions- A novel and generic network architecture called AANets specially designed

for tackling the stability-plasticity dilemma in CIL tasks;- A BOP-based formulation and its corresponding solution for end-to-end

training of the two types of parameters in AANets;- Extensive experiments on three benchmarks by plugging AANets in four

baseline methods.

Contributions

● Conceptual Illustrations

● The Architecture of AANets

● Optimization Steps- The formulation of bilevel optimization program (BOP)

- Updating Parameters

- Denotations : parameters for the stable and plastic blocks; : aggregation weights; : exemplars; : new data.

● Ablation Study

● Comparing w/ SOTA