Catalog Systems Recent method papers — theorized, no distinct product RIKEN Tensor Decomposition Incremental Learning

RIKEN Tensor Decomposition Incremental Learning

https://icml.cc/virtual/2025/poster/44196

Uses tensor decomposition to exploit low intrinsic dimensionality and pixel correlation of stored exemplar images, achieving high compression while preserving discriminative information for class-incremental learning. Compresses the replay buffer used in catastrophic-forgetting prevention. RIKEN AIP + Guangdong U. of Technology.

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 17 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Tensor-decomposed exemplar compression vendor fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2025 (ICML 2025 poster; exact arxiv submission date not confirmed) vendor fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
not applicable — not commercial fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix

Memory taxonomy

storage
parametric
retrieval
parametric-recall
persistence
parametric-permanent
update
parametric-edit
unit
episode
governance
n/a
conflict
n/a

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

Principled mathematical basis (tensor low-rank structure) rather than heuristic pruning; applies to any replay-based continual learning setup.

Cons

Targets image classification exemplar buffers specifically; applicability to language model continual learning is indirect and not demonstrated in this paper.

Claims & capabilities

ICML 2025 poster; compression without proportional accuracy loss vs full-exemplar replay.

Technical surface

API surface
not applicable — research paper
Backend storage
not applicable — research paper
Deployment
not applicable — not a deployable product
Embedding model
not applicable — research paper
Multi-tenancy
not applicable — research paper
MCP
not applicable — research paper, no deployed product
A2A
not applicable — research paper, no deployed product
OpenTelemetry
not applicable — research paper, no deployed product

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Sources (16 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).