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Found 9 papers, 0 papers with code
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images
Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases.
Localization-Aware Active Learning for Object Detection
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification.
Semi-supervised Acoustic Event Detection based on tri-training
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset.
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based acoustic event detection (AED) models.
Compression of Acoustic Event Detection Models With Quantized Distillation
Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems.
Few-shot acoustic event detection via meta-learning
We study few-shot acoustic event detection (AED) in this paper.
On Front-end Gain Invariant Modeling for Wake Word Spotting
Since the WW model is trained with the AFE-processed audio data, its performance is sensitive to AFE variations, such as gain changes.
Multi-Task Self-Supervised Pre-Training for Music Classification
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Federated Self-Supervised Learning for Acoustic Event Classification
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization.
