# Assessor-Guided Learning for Continual Environments

arXiv: https://arxiv.org/abs/2303.11624 · HTML: https://mlsanity.com/paper/2303.11624/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Meta-Learning Representations for Continual Learning](https://mlsanity.com/paper/1905.12588/) (2019)
- [Class-incremental Learning via Deep Model Consolidation](https://mlsanity.com/paper/1903.07864/) (2019)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [CPR: Classifier-Projection Regularization for Continual Learning](https://mlsanity.com/paper/2006.07326/) (2020)
- [Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning](https://mlsanity.com/paper/2012.04324/) (2020)
- [Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control](https://mlsanity.com/paper/1907.13322/) (2019)
- [Reinforced Continual Learning for Graphs](https://mlsanity.com/paper/2209.01556/) (2022)
- [Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach](https://mlsanity.com/paper/2106.14563/) (2021)
- [Unsupervised Continual Learning in Streaming Environments](https://mlsanity.com/paper/2109.09282/) (2021)
- [Continual Learning via Inter-Task Synaptic Mapping](https://mlsanity.com/paper/2106.13954/) (2021)
- [Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration](https://mlsanity.com/paper/2112.08132/) (2021)

## Built on by (most cited first, 7 of 7)

- [Few-Shot Class Incremental Learning via Robust Transformer Approach](https://mlsanity.com/paper/2405.05984/) (2024)
- [PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning](https://mlsanity.com/paper/2407.20705/) (2024)
- [Continual Knowledge Consolidation LORA for Domain Incremental Learning](https://mlsanity.com/paper/2510.16077/) (2025)
- [Cross-Domain Continual Learning via CLAMP](https://mlsanity.com/paper/2405.07142/) (2024)
- [Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2406.18574/) (2024)
- [Source-Free Cross-Domain Continual Learning](https://mlsanity.com/paper/2510.01649/) (2025)
- [Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation](https://mlsanity.com/paper/2606.30190/) (2026)

## Abstract (arXiv)

This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner. It performs a soft-weighting mechanism of every sample accepting positive samples while rejecting negative samples. The training objective of a base learner is to minimize a meta-weighted combination of the cross entropy loss function, the dark experience replay (DER) loss function and the knowledge distillation loss function whose interactions are controlled in such a way to attain an improved performance. A compensated over-sampling (COS) strategy is developed to overcome the class imbalanced problem of the episodic memory due to limited memory budgets. Our approach, Assessor-Guided Learning Approach (AGLA), has been evaluated in the class-incremental and task-incremental learning problems. AGLA achieves improved performances compared to its competitors while the theoretical analysis of the COS strategy is offered. Source codes of AGLA, baseline algorithms and experimental logs are shared publicly in urlhttps://github.com/anwarmaxsum/AGLA for further study.