Deep neural networks (DNNs) have been successfully applied in many safety-critical tasks, such asautonomous driving,facerecognition andverification,etc. Forreal-worldapplications, theDNN model aswell as the training dataset, are often hidden from users. Here, we analyze the computational cost of our method. The adversarial example generation process is conducted based on the offline surrogate models. Toachievethis goal, the authors multiply 16 skip connection by the random scalarr sampled from a uniform distribution.

Some previous papers (e.g., MIM [7])claimedthat ensemble with weighted logits (equation (4) in main text) outperforms ensemble with weighted probabilities and weighted combination of loss (equations(3)and(5)in main text). In our experiments, shown in Figure 6b, we observe that weighted combination of surrogate loss functions provide similar or even higher fooling rate compared to weighted probabilities or logits.

Overconfidence in deep neural networks could easily lead to deployments where predictions are made that should have been withheld. Figure 7: ResNet-50 trained onCIFAR-10 using focal lossγ = 0,3,4,5. Similarly, the confidence of the top predicted classˆy (for the training sample) isdenoted byˆptrain,top and theaverage equivalent inabinbyCtrain,top. Forthe training set, we care only about the confidence ofthe "true class"ˆptrain,true asthat isthe quantity which gets manipulated by some loss function. For validation set, on the other hand, we care about the confidence of the "top predicted class".

Abstract--The utility of deep learning models, such as CheXNet, in high stakes clinical settings is fundamentally constrained by their purely deterministic nature, failing to provide reliable measures of predictive confidence. Initial architectural development failed to stabilize performance and calibration using Monte Carlo Dropout (MCD), yielding an unacceptable Expected Calibration Error (ECE) of 0.7588. This technical failure necessitated a rigorous architectural pivot to a high diversity, 9-member Deep Ensemble (DE). This resulting DE successfully stabilized performance and delivered superior reliability, achieving a State-of-the-Art (SOT A) average Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8559 and an average F1 Score of 0.3857. Crucially, the DE demonstrated superior calibration (Mean ECE of 0.0728 and Negative Log-Likelihood (NLL) of 0.1916) and enabled the reliable decomposition of total uncertainty into its Aleatoric (irreducible data noise) and Epistemic (reducible model knowledge) components, with a mean Epistemic Uncertainty (EU) of 0.0240. These results establish the Deep Ensemble as a trustworthy and explainable platform, transforming the model from a probabilistic tool into a reliable clinical decision support system. Deep learning has achieved remarkable diagnostic accuracy in medical imaging, exemplified by the CheXNet model, which reached radiologist-level performance for pneumonia detection and classification across 14 thoracic pathologies.

Assessing fish freshness is vital for ensuring food safety and minimizing economic losses in the seafood industry. However, traditional sensory evaluation remains subjective, time-consuming, and inconsistent. Although recent advances in deep learning have automated visual freshness prediction, challenges related to accuracy and feature transparency persist. This study introduces a unified three-stage framework that refines and leverages deep visual representations for reliable fish freshness assessment. First, five state-of-the-art vision architectures - ResNet-50, DenseNet-121, EfficientNet-B0, ConvNeXt-Base, and Swin-Tiny - are fine-tuned to establish a strong baseline. Next, multi-level deep features extracted from these backbones are used to train seven classical machine learning classifiers, integrating deep and traditional decision mechanisms. Finally, feature selection methods based on Light Gradient Boosting Machine (LGBM), Random Forest, and Lasso identify a compact and informative subset of features. Experiments on the Freshness of the Fish Eyes (FFE) dataset demonstrate that the best configuration combining Swin-Tiny features, an Extra Trees classifier, and LGBM-based feature selection achieves an accuracy of 85.99%, outperforming recent studies on the same dataset by 8.69-22.78%. These findings confirm the effectiveness and generalizability of the proposed framework for visual quality evaluation tasks.

Supplementary material: Hold me tight! A Theoretical margin distribution of a linear classifier 2 B Examples of frequency "flipped" images 4 C Invariance and elasticity on MNIST data 4 D Connections to catastrophic forgetting 5 E Examples of filtered images 6 F Subspace sampling of the DCT 6 G Training parameters 7 H Cross-dataset performance 8 I Margin distribution for standard networks 9 J Adversarial training parameters 13 K Description of L2-PGD attack on frequency "flipped" data 14 L Spectral decomposition on frequency "flipped" data 15 M Margin distribution for adversarially trained networks 16 N Margin distribution on random subspaces 19 We demonstrate this effect in practice by repeating the experiment of Sec. MLP we use a simple logistic regression (see Table S1).Clearly, although the values along Figure S1 shows a few example images of the frequency "flipped" versions of the standard computer We further validate our observation of Section 3.2.2 that small margin do indeed corresponds to After this, we continue training the network with a linearly decaying learning rate (max. Figure S4: Filtered image examples. Table S2 shows the performance and training parameters of the different networks used in the paper.

COVID19 took the world by storm since December 2019. A highly infectious communicable disease, COVID19 is caused by the SARSCoV2 virus. By March 2020, the World Health Organization (WHO) declared COVID19 as a global pandemic. A pandemic in the 21st century after almost 100 years was something the world was not prepared for, which resulted in the deaths of around 1.6 million people worldwide. The most common symptoms of COVID19 were associated with the respiratory system and resembled a cold, flu, or pneumonia. After extensive research, doctors and scientists concluded that the main reason for lives being lost due to COVID19 was failure of the respiratory system. Patients were dying gasping for breath. Top healthcare systems of the world were failing badly as there was an acute shortage of hospital beds, oxygen cylinders, and ventilators. Many were dying without receiving any treatment at all. The aim of this project is to help doctors decide the severity of COVID19 by reading the patient's Computed Tomography (CT) scans of the lungs. Computer models are less prone to human error, and Machine Learning or Neural Network models tend to give better accuracy as training improves over time. We have decided to use a Convolutional Neural Network model. Given that a patient tests positive, our model will analyze the severity of COVID19 infection within one month of the positive test result. The severity of the infection may be promising or unfavorable (if it leads to intubation or death), based entirely on the CT scans in the dataset.

For real-world applications, the DNN model as well as the training dataset, are often hidden from users. We conducted all experiments in an Nvidia-V100 GPU. Imagenet is licensed under Custom (noncommercial). For ResNet-50, DenseNet-121, VGG-16, Inception-v3, we adopt the pre-trained models provided by torchvision package. For SI and Admix, we adopt the parameters suggested in Wang et al.

Pneumonia, particularly when induced by diseases like COVID-19, remains a critical global health challenge requiring rapid and accurate diagnosis. This study presents a comprehensive comparison of traditional machine learning and state-of-the-art deep learning approaches for automated pneumonia detection using chest X-rays (CXRs). We evaluate multiple methodologies, ranging from conventional machine learning techniques (PCA-based clustering, Logistic Regression, and Support Vector Classification) to advanced deep learning architectures including Convolutional Neural Networks (Modified LeNet, DenseNet-121) and various Vision Transformer (ViT) implementations (Deep-ViT, Compact Convolutional Transformer, and Cross-ViT). Using a dataset of 5,856 pediatric CXR images, we demonstrate that Vision Transformers, particularly the Cross-ViT architecture, achieve superior performance with 88.25% accuracy and 99.42% recall, surpassing traditional CNN approaches. Our analysis reveals that architectural choices impact performance more significantly than model size, with Cross-ViT's 75M parameters outperforming larger models. The study also addresses practical considerations including computational efficiency, training requirements, and the critical balance between precision and recall in medical diagnostics. Our findings suggest that Vision Transformers offer a promising direction for automated pneumonia detection, potentially enabling more rapid and accurate diagnosis during health crises.