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Found 7 papers, 1 papers with code
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
This paper proposes a progressive distillation method based on masked generation features for KGC task, aiming to significantly reduce the complexity of pre-trained models.
DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection
Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals.
SFUSNet: A Spatial-Frequency domain-based Multi-branch Network for diagnosis of Cervical Lymph Node Lesions in Ultrasound Images
The objective of this work is to diagnose cervical lymph node lesions in ultrasound images by leveraging a deep learning model.
U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images
Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.
Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection
The main purpose of this system is to improve the model's ability to learn precise forgery information for FSD task in low-quality scenarios.
Learning From Yourself: A Self-Distillation Method for Fake Speech Detection
To address this problem, we propose using the deepest network instruct shallow network for enhancing shallow networks.
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features
Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately.
