Self-supervised learning plays an important role in current state-of-the-art semi-supervised learning (SSL) methods. These methods learn inter-class heterogeneity among data and generate pseudo-labels based on class level representations. However, they often neglect intra-class heterogeneity, resulting in the under-exploitation of finer-grained semantic relationships within classes. To address this limitation, we introduce PivotAlign, a novel SSL approach that aims to 1) learn hierarchical representations to detect both interclass and intra-class semantic relationships, and 2) refine pseudo-labels based on learned representations with a class-debiasing strategy. Specifically, we first learn a set of pivots as sub-prototypes of classes. We then train representations so that features align with the assigned pivot and are hierarchically grouped based on both inter-class and intra-class heterogeneity. This allows us to capture both inter-class and intra-class semantic relationships among data and leverage them to better assign and refine pseudo-labels. Additionally, since SSL methods are prone to bias toward classes that are easier to learn, we further re-balance class predictions to alleviate this class bias. We demonstrate the effectiveness of PivotAlign on various SSL benchmarks, where PivotAlign achieves state-of-the-art performances. The source code will be released upon publication of the work.

Trojan attacks raise serious security concerns.In this paper, we investigate the underlying mechanism of Trojaned BERT models.We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trigger token hijacks the attention focus regardless of the context.We provide a thorough qualitative and quantitative analysis of this phenomenon, revealing insights into the Trojan mechanism.Based on the observation, we propose an attention-based Trojan detector to distinguish Trojaned models from clean ones.To the best of our knowledge, this is the first paper to analyze the Trojan mechanism and to develop a Trojan detector based on the transformer's attention 1 .

Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, which can later be exploited. Guided by basic neuroscientific principles, we discover subtle -- yet critical -- structural deviation characterizing Trojaned models. In our analysis we use topological tools. They allow us to model high-order dependencies in the networks, robustly compare different networks, and localize structural abnormalities. One interesting observation is that Trojaned models develop short-cuts from shallow to deep layers. Inspired by these observations, we devise a strategy for robust detection of Trojaned models. Compared to standard baselines it displays better performance on multiple benchmarks.

To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on the noisy classifiers (i.e., models trained on the noisy training data) to determine whether a label is trustworthy. However, it remains unknown why this heuristic works well in practice. In this paper, we provide the first theoretical explanation for these methods. We prove that the prediction of a noisy classifier can indeed be a good indicator of whether the label of a training data is clean. Based on the theoretical result, we propose a novel algorithm that corrects the labels based on the noisy classifier prediction. The corrected labels are consistent with the true Bayesian optimal classifier with high probability. We incorporate our label correction algorithm into the training of deep neural networks and train models that achieve superior testing performance on multiple public datasets.

Noisy labels can impair the performance of deep neural networks. To tackle this problem, in this paper, we propose a new method for filtering label noise. Unlike most existing methods relying on the posterior probability of a noisy classifier, we focus on the much richer spatial behavior of data in the latent representational space. By leveraging the high-order topological information of data, we are able to collect most of the clean data and train a high-quality model. Theoretically we prove that this topological approach is guaranteed to collect the clean data with high probability. Empirical results show that our method outperforms the state-of-the-arts and is robust to a broad spectrum of noise types and levels.