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Found 21 papers, 2 papers with code
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Class-Aware Generative Adversarial Transformers for Medical Image Segmentation
In this work, we present CASTformer, a novel type of generative adversarial transformers, for 2D medical image segmentation.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
Self-supervised Dialogue Learning for Spoken Conversational Question Answering
We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence.
Undistillable: Making A Nasty Teacher That CANNOT teach students
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.
Audio Signal Processing
Conversational Question Answering
+2
Knowledge Distillation for Improved Accuracy in Spoken Question Answering
However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.
Automatic Speech Recognition
Conversational Question Answering
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation
However, the well-trained models often fail in the target domain due to the domain shift.
Layer-Wise Multi-View Decoding for Improved Natural Language Generation
While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.
Low-Dose CT via Deep CNN with Skip Connection and Network in Network
The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application.
Super-resolution MRI through Deep Learning
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.
Medical Physics
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
