Test Time Adaptation involves making adjustments to a model’s parameters or decision-making processes based on the characteristics of the data it encounters at test time. Test time adaptation can be employed to improve the model’s performance in situations that may differ from the training data, ensuring better generalization and robustness.
Mix-CLAP from NEC Laboratories America delivers near-Transformer accuracy for sound event classification at a fraction of the compute cost, using dual lightweight encoders and adaptive, noise-aware inference for real-world edge deployment.
Real-world deployment requires sound event and acoustic scene classification systems to remain reliable in noisy, diverse environments on resource-constrained devices. Although contrastive language-audio pretraining (CLAP) models with Transformer-based audio encoders achieve strong zero-shot performance, their computational cost hinders deployment. In this paper, we propose Mix-CLAP, a computationally efficient, noise-aware CLAP model with knowledge-distilled audio encoders. Our method includes: (1) a two-stage knowledge distillation from teacher embeddings to two lightweight student encoders?one on clean audio, the other on noisy audio, and (2) adaptive inference that combines their embeddings together with a fusion parameter and minimizes the parameterized entropy at test time. Experiments show that Mix-CLAP with MobileNetV3-based audio encoders greatly improves computational efficiency, while achieving a comparable average accuracy of 52.58% to the Transformer-based CLAP model at 52.83% on the recorded ESC50 datasets with different devices including microphones and fiber-optic distributed acoustic sensors under diverse conditions, making it suitable for real-world, resource-constrained applications.
In the field of histopathology, computer-aided systems face significant challenges due to diverse domain shifts. They include variations in tissue source organ, preparation and scanningprotocols. These domain shifts can significantly impact algorithms performance in histopathology tasks, such as cancer segmentation. In this paper, we address this problem byproposing a new multi-task extension of test-time adaptation (TTA) for simultaneous semantic, and instance segmentation of nuclei. First, to mitigate domain shifts during testing, weuse a feature alignment TTA method, through which we adapt the feature vectors of the target data based on the feature vectors statistics derived from the source data. Second, the ratioof Gradient norm to Parameter norm (G2P) is proposed to guide the feature alignment procedure. Our approach requires a pre-trained model on the source data, without requiringaccess to the source dataset during TTA. This is particularly crucial in medical applications where access to training data may be restricted due to privacy concerns or patient consent. Through experimental validation, we demonstrate that the proposed method consistently yields competitive results when applied to out-of-distribution data across multiple datasets.
Princeton Office
4 Independence Way, Suite 200
Princeton, NJ 08540
+1 609-520-1555
San Jose Office
2033 Gateway Place, Suite 200
San Jose, CA 95110
+1 408-863-6007
NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.