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Found 9 papers, 5 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
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.
Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation.
Ranked #4 on
Domain Generalization
on GTA5-to-Cityscapes
Design and Development of Autonomous Delivery Robot
Autonomous robots are developed to be robust enough to work beside humans and to carry out jobs efficiently.
Data Efficient Stagewise Knowledge Distillation
We show, across the tested tasks, significant performance gains even with a fraction of the data used in distillation, without compromising on the metric.
