Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
This paper presents a simple yet effective approach that improves continual test-time
adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge …
adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge …
2pcnet: Two-phase consistency training for day-to-night unsupervised domain adaptive object detection
M Kennerley, JG Wang… - Proceedings of the …, 2023 - openaccess.thecvf.com
Object detection at night is a challenging problem due to the absence of night image
annotations. Despite several domain adaptation methods, achieving high-precision results …
annotations. Despite several domain adaptation methods, achieving high-precision results …
Trustworthy representation learning across domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation
Abstract Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
Deliberated domain bridging for domain adaptive semantic segmentation
In unsupervised domain adaptation (UDA), directly adapting from the source to the target
domain usually suffers significant discrepancies and leads to insufficient alignment. Thus …
domain usually suffers significant discrepancies and leads to insufficient alignment. Thus …
A Survey of Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Black-box unsupervised domain adaptation with bi-directional atkinson-shiffrin memory
Black-box unsupervised domain adaptation (UDA) learns with source predictions of target
data without accessing either source data or source models during training, and it has clear …
data without accessing either source data or source models during training, and it has clear …
On the robustness of open-world test-time training: Self-training with dynamic prototype expansion
Generalizing deep learning models to unknown target domain distribution with low latency
has motivated research into test-time training/adaptation (TTT/TTA). Existing approaches …
has motivated research into test-time training/adaptation (TTT/TTA). Existing approaches …
Periodically exchange teacher-student for source-free object detection
Source-free object detection (SFOD) aims to adapt the source detector to unlabeled target
domain data in the absence of source domain data. Most SFOD methods follow the same …
domain data in the absence of source domain data. Most SFOD methods follow the same …
Prior knowledge guided unsupervised domain adaptation
The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …
attractive technique in many real-world applications, though it also brings great challenges …