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Found 14 papers, 5 papers with code
Exploiting Inherent Class Label: Towards Robust Scribble Supervised Semantic Segmentation
Scribble-based weakly supervised semantic segmentation leverages only a few annotated pixels as labels to train a segmentation model, presenting significant potential for reducing the human labor involved in the annotation process.
Interference-Aware Super-Constellation Design for NOMA
Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique.
V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer
Concept Bottleneck Models (CBMs) offer inherent interpretability by initially translating images into human-comprehensible concepts, followed by a linear combination of these concepts for classification.
Multi-Level Correlation Network For Few-Shot Image Classification
In this paper, we argue that measure at such a level may not be effective enough to generalize from base to novel classes when using only a few images.
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation
However, the incorrect pseudo-labels may corrupt the learned feature and lead to a new problem that the better the model is trained on the old task, the poorer the model performs on the new tasks.
Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label
In this study, we propose a class-driven scribble promotion network, which utilizes both scribble annotations and pseudo-labels informed by image-level classes and global semantics for supervision.
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI
The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI.
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Moreover, we develop a multi-level contrastive learning strategy that integrates correspondences across feature-level, image-level, and pixel-level representations to ensure the encoder and decoder capture comprehensive details from representations of varying scales and granularities during the pre-training phase.
3D Implicit Transporter for Temporally Consistent Keypoint Discovery
To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation
Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch.
Fully-integrated multipurpose microwave frequency identification system on a single chip
We demonstrate a fully-integrated multipurpose microwave frequency identification system on silicon-on-insulator platform.
SVD-Embedded Deep Autoencoder for MIMO Communications
SVDembedded DAE largely outperforms theoretic linear precoding in terms of BER.
Multi-Objective DNN-based Precoder for MIMO Communications
Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99. 45\% of the averaged optimal solutions).
Deep Learning based Precoding for the MIMO Gaussian Wiretap Channel
A novel precoding method based on supervised deep neural networks is introduced for the multiple-input multiple-output Gaussian wiretap channel.
