Ideally, we want networks to be accurate, calibrated and confident. We show that, as opposed to the standard cross-entropy loss, focal loss [19] allows us to learn models that are already very well calibrated. When combined with temperature scaling, whilst preserving accuracy, it yields state-of-the-art calibrated models. We provide a thorough analysis of the factors causing miscalibration, and use the insights we glean from this to justify the empirically excellent performance of focal loss.

Additionally, we provide the calibration performance of various competitive approaches. Briefly, calibration quantifies how similar a model's confidence and its accuracy are [Osborne, 1991]). To measure it, we employ the recently proposed Adaptive ECE (AdaECE) [Mukhoti et al., 2020]. For all the methods, the AdaECE is computed after performing temperature scaling [Guoetal.,2017] Unfortunately, we could not manage to make their code work on C100 as the training procedure seemed to be unstable.

Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overconfident, posing risks for deployment in safety-critical scenarios. Current post-hoc calibration methods face a fundamental dilemma: global approaches like Temperature Scaling apply uniform adjustments across all samples, introducing high bias despite computational efficiency, while more expressive methods that operate on full logit distributions suffer from high variance due to noisy high-dimensional inputs and insufficient validation data. To address these challenges, we propose Sample Margin-Aware Recalibration of Temperature (SMART), a lightweight, data-efficient recalibration method that precisely scales logits based on the margin between the top two logits -- termed the logit gap. Specifically, the logit gap serves as a denoised, scalar signal directly tied to decision boundary uncertainty, providing a robust indicator that avoids the noise inherent in high-dimensional logit spaces while preserving model prediction invariance. Meanwhile, SMART employs a novel soft-binned Expected Calibration Error (SoftECE) objective that balances model bias and variance through adaptive binning, enabling stable parameter updates even with extremely limited calibration data. Extensive evaluations across diverse datasets and architectures demonstrate that SMART achieves state-of-the-art calibration performance even with substantially fewer parameters compared to existing parametric methods, offering a principled, robust, and highly efficient solution for practical uncertainty quantification in neural network predictions. The source code is available at: https://anonymous.4open.science/r/SMART-8B11.

After the revelation that neural networks tend to produce overconfident predictions, the problem of calibration, which aims to align confidence with accuracy to enhance the reliability of predictions, has gained significant importance. Several solutions based on calibration maps have been proposed to address the problem of recalibrating a trained classifier using additional datasets. In this paper, we offer an algorithm that transforms the weights of the last layer of the classifier, distinct from the calibration-map-based approach. We concentrate on the geometry of the final linear layer, specifically its angular aspect, and adjust the weights of the corresponding layer. We name the method Tilt and Average(\textsc{Tna}), and validate the calibration effect empirically and theoretically. Through this, we demonstrate that our approach, in addition to the existing calibration-map-based techniques, can yield improved calibration performance. Code available : https://github.com/GYYYYYUUUUU/TNA_Angular_Scaling.

We show that the effectiveness of the well celebrated Mixup [Zhang et al., 2018] can be further improved if instead of using it as the sole learning objective, it is utilized as an additional regularizer to the standard cross-entropy loss. This simple change not only provides much improved accuracy but also significantly improves the quality of the predictive uncertainty estimation of Mixup in most cases under various forms of covariate shifts and out-of-distribution detection experiments. In fact, we observe that Mixup yields much degraded performance on detecting out-of-distribution samples possibly, as we show empirically, because of its tendency to learn models that exhibit high-entropy throughout; making it difficult to differentiate in-distribution samples from out-distribution ones. To show the efficacy of our approach (RegMixup), we provide thorough analyses and experiments on vision datasets (ImageNet & CIFAR-10/100) and compare it with a suite of recent approaches for reliable uncertainty estimation.

Following the surge of popularity of Transformers in Computer Vision, several studies have attempted to determine whether they could be more robust to distribution shifts and provide better uncertainty estimates than Convolutional Neural Networks (CNNs). The almost unanimous conclusion is that they are, and it is often conjectured more or less explicitly that the reason of this supposed superiority is to be attributed to the self-attention mechanism. In this paper we perform extensive empirical analyses showing that recent state-of-the-art CNNs (particularly, ConvNeXt [20]) can be as robust and reliable or even sometimes more than the current state-of-the-art Transformers. However, there is no clear winner. Therefore, although it is tempting to state the definitive superiority of one family of architectures over another, they seem to enjoy similar extraordinary performances on a variety of tasks while also suffering from similar vulnerabilities such as texture, background, and simplicity biases.