Search Results for author:
Found 17 papers, 2 papers with code
Recurrent Neural Network Training with Dark Knowledge Transfer
Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Speaker Vectors for Semi Text-independent Speaker Verification
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification.
Knowledge Transfer Pre-training
Compared to the conventional layer-wise methods, this new method does not care about the model structure, so can be used to pre-train very complex models.
Recognize Foreign Low-Frequency Words with Similar Pairs
We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN).
Machine learning driven synthesis of few-layered WTe2
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.
Large-scale Transfer Learning for Low-resource Spoken Language Understanding
However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations
With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic.
An Iterative Labeling Method for Annotating Fisheries Imagery
In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces.
Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling.
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition
We also find that in joint CTC-Attention ASR model, decoder is more sensitive to linguistic information than acoustic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Face Forgery Detection Based on Facial Region Displacement Trajectory Series
We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement.
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
An atrium segmentation network with location guidance and siamese adjustment
The segmentation of atrial scan images is of great significance for the three-dimensional reconstruction of the atrium and the surgical positioning.
Generalized Deepfakes Detection with Reconstructed-Blended Images and Multi-scale Feature Reconstruction Network
The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries.
Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey
To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation, including both single-person and multi-person approaches, as well as human mesh recovery, encompassing methods based on explicit models and implicit representations.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy.
