Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To address this issue, we propose a simple yet effective approach called Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping, estimated by modeling the distributions of clean and noisy samples. This approach allows us to modify the clipping threshold at each training step, effectively controlling the influence of noise gradients. Additionally, we provide statistical analysis to certify the noise-tolerance ability of OGC. Our extensive experiments across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise, demonstrate the effectiveness of our approach.

The Influence Function (IF) is a widely used technique for assessing the impact of individual training samples on model predictions. However, existing IF methods often fail to provide reliable influence estimates in deep neural networks, particularly when applied to noisy training data. This issue does not stem from inaccuracies in parameter change estimation, which has been the primary focus of prior research, but rather from deficiencies in loss change estimation, specifically due to the sharpness of validation risk. In this work, we establish a theoretical connection between influence estimation error, validation set risk, and its sharpness, underscoring the importance of flat validation minima for accurate influence estimation. Furthermore, we introduce a novel estimation form of Influence Function specifically designed for flat validation minima. Experimental results across various tasks validate the superiority of our approach.

Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.

Camouflaged object detection (COD) is a promising yet challenging task that aims to segment objects concealed within intricate surroundings, a capability crucial for modern industrial applications. Current COD methods primarily focus on the direct fusion of high-level and low-level information, without considering their differences and inconsistencies. Consequently, accurately segmenting highly camouflaged objects in challenging scenarios presents a considerable problem. To mitigate this concern, we propose a novel framework called visual and semantic separation network (VSSNet), which separately extracts low-level visual and high-level semantic cues and adaptively combines them for accurate predictions. Specifically, it features the information extractor module for capturing dimension-aware visual or semantic information from various perspectives. The complementary information-selected module leverages the complementary nature of visual and semantic information for adaptive selection and fusion. In addition, the region disparity weighting strategy encourages the model to prioritize the boundaries of highly camouflaged and difficult-to-predict objects. Experimental results on benchmark datasets show the VSSNet significantly outperforms State-of-the-Art COD approaches without data augmentations and multiscale training techniques. Furthermore, our method demonstrates satisfactory cross-domain generalization performance in real-world industrial environments.

Although the previous works for out-of-distribution(OOD) detection have achieved great improvements, they are still highly dependent on the specific selection of the outliers from external datasets or that transformed by certain data augmentations, and hence cannot be applied in a wide range of domains. To solve this problem, in this paper, we propose a simple, yet effective method called Outlier Exposure by Simple Transformations (OEST), which aims at exposing the outliers by the composition of several simple transformations of data augmentations via energy score. In addition, our training scheme can make full use of nearly all considered data augmentations in previous works, even though some of them are generally regarded as useless. And we also find that, for simple data augmentation, our training scheme is less time-consuming and better in performance than relative works. Furthermore, our experiments validate that our method outperforms the state-of-the-art methods.

Abstract Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments. This paper proposes a Group‐aware Contrastive Network (GACN), a robust lightweight model, which extracts discriminative features by leveraging contrastive learning rather than increasing model parameters. Specifically, with a carefully designed contrastive loss function, GACN minimizes intra‐class distances and maximizes inter‐class distances between different age groups in feature space. Thus, faces belonging to the same age group are pulled together, while clusters of faces from different age groups are pushed apart. Unlike existing contrastive learning methods, which are separated from the downstream tasks, GACN integrates contrastive learning into age regression and jointly optimizes them for age representation learning. This allows to achieve robust age estimation using a lightweight network that is 1/662 of the model size of VGGNet. Extensive experiments on IMDB‐WIKI, Morph II, and FG‐NET demonstrate that the proposed method has a significant improvement over the baseline model and performs comparably to existing compact and bulky methods.