Recognizing out-of-distribution (OOD) samples is essential for deploying robust machine learning systems in open-world environments. While conventional OOD detection approaches rely on feature representations from the penultimate layer of neural networks, they often overlook informative signals embedded in intermediate layers. In this paper, we present a straightforward feature mixing approach for pretrained Transformers, which combines multi-layer representations via calculated importance weights, and identifies OOD samples using Mahalanobis distance in the blended feature space. When in-distribution samples are accessible, we show that parameter-efficient fine-tuning strategies effectively balance classification accuracy and OOD detection performance. We conduct extensive empirical analyses to validate the superiority of our proposed method under zero-shot, and fine-tuning settings using both class-balanced and long-tailed datasets. The source code is available at https://github.com/SEUML/X-Maha.

Setup We additionally evaluate GradNorm on a common benchmark with CIFAR-10 and CIFAR100 [22] as ID datasets, which is routinely used in literature [13, 27, 14, 29, 26]. We use the standard split with 50,000 training images and 10,000 test images. The learning rate is initially 0.1, and decays by a factor of 10 at epochs 50, 75 and 90 respectively. Results We summarize the results in Table 6, where GradNormremains competitive. In particular, GradNorm reduces the average FPR95 by 8.77% on CIFAR-10 compared to the best baseline.

Out-of-distribution (OOD) detection is essential for model trustworthiness which aims to sensitively identity semantic OOD samples and robustly generalize for covariate-shifted OOD samples. However, we discover that the superior OOD detection performance of state-of-the-art methods is achieved by secretly sacrificing the OOD generalization ability. The classification accuracy frequently collapses catastrophically when even slight noise is encountered. Such a phenomenon violates the motivation of trustworthiness and significantly limits the model's deployment in the real world. What is the hidden reason behind such a limitation?

Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy to sharpen the decision boundary between the in-distribution and OOD data.