Bayesian Exploration of Pre-trained Models for Low-shot Image Classification

Low-shot image classification is a fundamental task in computer vision, and the emergence of large-scale vision-language models such as CLIP has greatly advanced the forefront of research in this field. However, most existing CLIP-based methods lack the flexibility to effectively incorporate other pre-trained models that encompass knowledge distinct from CLIP. To bridge the gap, this work proposes a simple and effective probabilistic model ensemble framework based on Gaussian processes, which have previously demonstrated remarkable efficacy in processing small data. We achieve the integration of prior knowledge by specifying the mean function with CLIP and the kernel function with an ensemble of deep kernels built upon various pre-trained models. By regressing the classification label directly, our framework enables analytical inference, straightforward uncertainty quantification, and principled hyper-parameter tuning. Through extensive experiments on standard benchmarks, we demonstrate that our method consistently outperforms competitive ensemble baselines regarding predictive performance. Additionally, we assess the robustness of our method and the quality of the yielded uncertainty estimates on out-of-distribution datasets. We also illustrate that our method, despite relying on label regression, still enjoys superior model calibration compared to most deterministic baselines

Effects of different remifentanil doses on the stress reaction and BIS value of video laryngoscope-guided tracheal intubation

Purpose: To explore the affinity of different remifentanil doses for  intravenous anesthesia in video laryngoscope-guided tracheal intubation.Methods: Eighty patients who required anesthesia for elective non-ophthalmic surgery were included. They were divided into four groups (A, B, C and D) and received a different dose of either 1, 1.5, or 2 μg/kg remifentanil or a dose of 2 μg/kg fentanyl, respectively. An anesthetic state was achieved and maintained by administration of 3 - 5 mg/kg propofolum and 0.1 - 0.3 mg/kg remifentanil. The mean value of the various indices, including arterial pressure (MAP), bispectral index and heart rate (HR) wererecorded prior to anesthesia induction (T0), prior to intubation (T1),  instantly before intubation (T2), and at 1 (T3), 3 (T4) and 5 (T5) after the intubation. Cortisol concentration was measured at T0, T1 and T5.Results: Remifentanil (1 μg/kg) induced a moderate increase in HR and MAP at T3 compared with fentanyl. HR and MAP in the lower dose group were significantly higher than those in groups B and C at T3. Compared to T1, the concentrations of cortisol decreased after anesthesia and then significantly increased during tracheal intubation. Cortisol concentration in group B was the lowest at T5.Conclusion: The most effective concentrations of remifentanil are 1 and 1.5 μg/kg for anesthesia induction and tracheal intubation, respectively.Keywords: Remifentanil, Stress reaction, Bispectral index, Video laryngoscope, Tracheal intubatio

Multidimensional Uncertainty-Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.Comment: AAAI 202