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Found 11 papers, 4 papers with code
The Effect of Pretraining on Extractive Summarization for Scientific Documents
Large pretrained models have seen enormous success in extractive summarization tasks.
A Closer Look at Bearing Fault Classification Approaches
Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations.
Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021
This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge.
SpliceOut: A Simple and Efficient Audio Augmentation Method
Time masking has become a de facto augmentation technique for speech and audio tasks, including automatic speech recognition (ASR) and audio classification, most notably as a part of SpecAugment.
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image.
Image Augmentation
Semi Supervised Learning for Image Captioning
ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned.
High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection
In this work, we devise a curriculum-learning-based training process for object boundary detection.
Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.
