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Found 9 papers, 3 papers with code
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain.
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
We are the first to utilize vision transformers for domain adaptation in a privacy-oriented source-free setting, and our approach achieves state-of-the-art performance on single-source, multi-source, and multi-target benchmarks
Multi-Level Compositional Reasoning for Interactive Instruction Following
At the middle level, we discriminatively control the agent's navigation by a master policy by alternating between a navigation policy and various independent interaction policies.
Subsidiary Prototype Alignment for Universal Domain Adaptation
Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift.
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data.
Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation
However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination.
Hierarchical Modular Framework for Long Horizon Instruction Following
To address such composite tasks, we propose a hierarchical modular approach to learn agents that navigate and manipulate objects in a divide-and-conquer manner for the diverse nature of the entailing tasks.
Factorizing Perception and Policy for Interactive Instruction Following
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents.
