Research map / Label-efficient and robust learning / Domain adaptation and generalization

Learning Informative Invariant Representations via Hierarchical Latent Decomposition

NeurIPS 2026 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Optimization-based domain generalization

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes BLENDER, a reconstruction-aware hierarchical latent decomposition framework for domain generalization that learns informative invariant representations.

Problem

Domain generalization methods that enforce domain-invariant representations may discard task-relevant information under domain shift.

Approach

Combines reconstruction-aware objectives with a hierarchical latent decomposition so that invariant representations retain sufficient task-relevant information, rather than relying on invariance alone.

Closest papers by meaning

• Failure Modes of Domain Generalization Algorithms (CVPR 2022 · similarity 0.90)
• Exploiting Domain-Specific Features to Enhance Domain Generalization (NeurIPS 2021 · similarity 0.90)
• A General Representation-Based Approach to Multi-Source Domain Adaptation (ICML 2025 · similarity 0.90)
• Enhancing Evolving Domain Generalization through Dynamic Latent Representations (AAAI 2024 · similarity 0.90)
• Domain Generalization via Conditional Invariant Representations (AAAI 2018 · similarity 0.90)
• Robust Domain Generalization under Divergent Marginal and Conditional Distributions (NeurIPS 2026 · similarity 0.88)