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Found 7 papers, 3 papers with code
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
We propose the prediction disagreement as the accuracy estimate, calculated by comparing the target model prediction with dropout inferences.
Collaborative Inference via Dynamic Composition of Tiny AI Accelerators on MCUs
The advent of tiny AI accelerators opens opportunities for deep neural network deployment at the extreme edge, offering reduced latency, lower power cost, and improved privacy in on-device ML inference.
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
LanSER: Language-Model Supported Speech Emotion Recognition
Speech emotion recognition (SER) models typically rely on costly human-labeled data for training, making scaling methods to large speech datasets and nuanced emotion taxonomies difficult.
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications
Our evaluation with four different IMU sensing tasks shows that IMG2IMU outperforms the baselines pre-trained on sensor data by an average of 9. 6%p F1-score, illustrating that vision knowledge can be usefully incorporated into IMU sensing applications where only limited training data is available.
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation.
DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing
However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i. e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing.
