---
title: "Drift-adaptive Representation Preservation for Domain-Incremental Learning"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06093v1"
permalink: "https://feed.astralor.com/items/itm_d70a1ad5a48a8647b278056a"
published: "2026-11-04T07:37:56.000Z"
score: 60
channel: "ai"
tags: ["RAG","多模态","Drift-adaptive Representation Preservation","Domain-Incremental Learning Pretrained","Domain-Incremental Learning","DIL","Pretrained"]
---

# Drift-adaptive Representation Preservation for Domain-Incremental Learning

## At a glance

Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often degrades performance on…

## Key points

- Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often degrades performance on…
- We observe that forgetting is not uniform across domains and classes and is not explained by training order alone.
- This motivates us to track each past domain-class concept individually.

## Full text

Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often degrades performance on previously learned ones. We observe that forgetting is not uniform across domains and classes and is not explained by training order alone. This motivates us to track each past domain-class concept individually. We use representation drift, defined as the deviation of a past concept's current representation from a fixed anchor, as a measurable surrogate for forgetting risk. The measured drift then serves two complementary roles. A consolidation loss assigns stronger preservation to high-drift pairs, while prototype construction down-weights highly drifted domains to reduce their influence on the class reference. Preservation is thus allocated by measured drift rather than assumed importance. To obtain this signal during training without storing past raw data, we repurpose a Joint-Embedding Predictive Architecture (JEPA). For each domain-class pair, we store only a compact context summary in the backbone feature space and rerun it through the learned context-to-target relation to measure its drift from the fixed anchor. Across DomainNet, Office-Home, CORe50, and CDDB-Hard, our method sustains a strong accuracy-forgetting balance on every benchmark, using substantially less training memory than accuracy-competitive methods and the smallest storage among compared methods.

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