Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing significant challenges for lightweight architectures with depth-wise convolutions. To address this, we propose a 1.58-bit convolution to enhance expressiveness and a pre-BN residual connection to stabilize optimization by improving the Hessian condition number. These innovations enable, to the best of our knowledge, the first successful binarization of depth-wise convolutions in BNNs. Our method achieves 33M OPs on ImageNet with MobileNet V1, establishing a new state-of-the-art in BNNs by outperforming prior methods with comparable OPs. Moreover, it consistently outperforms existing methods across various datasets, including CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet, and Oxford Flowers 102, with accuracy improvements of up to 9.3 percentage points.

Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain generalization (UDG), which aims to develop SSL models that can generate domain-irrelevant features, has been conducted. Most UDG approaches utilize contrastive learning with InfoNCE to generate representations, and perform feature alignment based on strong assumptions to generalize domain-irrelevant common features from multi-source domains. However, existing methods that rely on instance discrimination tasks are not effective at extracting domain-irrelevant common features. This leads to the suppression of domain-irrelevant common features and the amplification of domain-relevant features, thereby hindering domain generalization. Furthermore, strong assumptions underlying feature alignment can lead to biased feature learning, reducing the diversity of common features. In this paper, we propose a novel approach, DomCLP, Domain-wise Contrastive Learning with Prototype Mixup. We explore how InfoNCE suppresses domain-irrelevant common features and amplifies domain-relevant features. Based on this analysis, we propose Domain-wise Contrastive Learning (DCon) to enhance domain-irrelevant common features. We also propose Prototype Mixup Learning (PMix) to generalize domain-irrelevant common features across multiple domains without relying on strong assumptions. The proposed method consistently outperforms state-of-the-art methods on the PACS and DomainNet datasets across various label fractions, showing significant improvements.

Weakly-supervised Semantic Segmentation (WSSS) aims to provide a precise semantic segmentation results without expensive pixel-wise segmentation labels. With the supervision gap between classification and segmentation, Image-level WSSS mainly relies on Class Activation Maps (CAMs) from the classification model to emulate the pixel-wise annotations. However, CAMs often fail to cover the entire object region because classification models tend to focus on narrow discriminative regions in an object. Towards accurate CAM coverage, Existing WSSS methods have tried to boost feature representation learning or impose consistency regularization to the classification models, but still there are limitation in activating non-discriminative area, where the focus of the models is weak. To tackle this issue, we propose FSAE framework, which provides explicit supervision of non-discriminative area, encouraging the CAMs to activate on various object features. We leverage weak-strong consistency with pseudo-label expansion strategy for reliable supervision and enhance learning of non-discriminative object boundaries. Specifically, we use strong perturbation to make challenging inference target, and focus on generating reliable pixel-wise supervision signal for broad object regions. Extensive experiments on the WSSS benchmark datasets show that our method boosts initial seed quality and segmentation performance by large margin, achieving new state-of-the-art performance on benchmark WSSS datasets. Our public code is available at https://github.com/obeychoi0120/FSAE.

Deep Neural Networks (DNNs) have demonstrated remarkable performance across diverse domains and tasks with large-scale datasets. To reduce labeling costs for large-scale datasets, semi-automated and crowdsourcing labeling methods are developed, but their labels are in-evitably noisy. Learning with Noisy Labels (LNL) approaches aim to train DNNs despite the presence of noisy labels. These approaches utilize the memorization effect to select correct labels and refine noisy ones, which are then used for subsequent training. However, these methods en-counter a significant decrease in the model's generalization performance due to the inevitably existing noise labels. To overcome this limitation, we propose a new approach to enhance learning with noisy labels by incorporating additional distribution informationstructural labels. In order to leverage additional distribution information for generalization, we employ a reverse k-NN, which helps the model in achieving a better feature manifold and mitigating over-fitting to noisy labels. The proposed method shows outperformed performance in multiple benchmark datasets with IDN and real-world noisy datasets.

Semi-supervised learning (SSL) is a method to make better models using a large number of easily accessible unlabeled data along with a small number of labeled data obtained at a high cost. Most of existing SSL studies focus on the cases where sufficient amount of labeled samples are available, tens to hundreds labeled samples for each class, which still requires a lot of labeling cost. In this paper, we focus on SSL environment with extremely scarce labeled samples, only 1 or 2 labeled samples per class, where most of existing methods fail to learn. We propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias. In addition, for the realistic model selection in the absence of the validation dataset, we also propose a model selection method based on our propagation regularizer. The proposed methods show 70.9%, 30.3%, and 78.9% accuracy on CIFAR-10, CIFAR-100, SVHN dataset with just one labeled sample per class, which are improved by 8.9% to 120.2% compared to the existing approaches. And our proposed methods also show good performance on a higher resolution dataset, STL-10.