Search Results for author:
Found 18 papers, 5 papers with code
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers
The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network.
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
We find that the ensemble model overfitted to the training set shows sub-par calibration performance and also observe that PLMs trained with confidence penalty loss have a trade-off between calibration and accuracy.
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity
Recently, diabetic retinopathy (DR) screening utilizing ultra-wide optical coherence tomography angiography (UW-OCTA) has been used in clinical practices to detect signs of early DR.
Nonparametric Decoding for Generative Retrieval
The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed.
AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography
Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
Contrastive Siamese Network for Semi-supervised Speech Recognition
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition.
Reducing Streaming ASR Model Delay with Self Alignment
From LibriSpeech evaluation, self alignment outperformed existing schemes: 25% and 56% less delay compared to FastEmit and constrained alignment at the similar word error rate.
Deep learning-based citation recommendation system for patents
In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system.
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition
In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model.
Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis
Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history.
Software Engineering Human-Computer Interaction
End-to-End Multi-Task Denoising for the Joint Optimization of Perceptual Speech Metrics
Second, three loss functions based on SDR, PESQ and STOI are proposed to minimize the metric mismatch.
Sound Audio and Speech Processing
T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement
Transformer neural networks (TNN) demonstrated state-of-art performance on many natural language processing (NLP) tasks, replacing recurrent neural networks (RNNs), such as LSTMs or GRUs.
Audio and Speech Processing Sound
End-to-End Multi-Task Denoising for joint SDR and PESQ Optimization
First, the network optimization is performed on the time-domain signals after ISTFT to avoid spectrum mismatch.
Text Classification using Capsules
This paper presents an empirical exploration of the use of capsule networks for text classification.
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition
Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers.
