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Found 20 papers, 3 papers with code
Debiased Novel Category Discovering and Localization
In recent years, object detection in deep learning has experienced rapid development.
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping
To overcome this limitation, we introduce a strategy to map monaural speech into a fixed simulation space for better differentiation between target speech and noise.
A comparative study of Grid and Natural sentences effects on Normal-to-Lombard conversion
Then We compare natural and grid sentences in terms of Lombard effect and Normal-to-Lombard conversion using LCT and Enhanced MAndarin Lombard Grid corpus (EMALG).
PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement
Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications.
Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation
We observe that besides target positive information, e. g., ground-truth speech and features, the target negative information, such as interference signals and features, helps make pattern of target speech and interference signals more discriminative.
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement
In this study, we propose a SE model that integrates both speech positive and negative information for improving SE performance by adopting contrastive learning, in which two innovations have consisted.
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails
In this paper we present research results concerning the application of Active Learning to anomaly detection in redacted emails, comparing the utility of different methods for implementing active learning in this context.
Pruning Deep Neural Networks from a Sparsity Perspective
Our extensive experiments corroborate the hypothesis that for a generic pruning procedure, PQI decreases first when a large model is being effectively regularized and then increases when its compressibility reaches a limit that appears to correspond to the beginning of underfitting.
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement
Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems.
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation
To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions.
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation
One problem is understanding if a network is more compressible than another of the same structure.
Targeted Cross-Validation
A standard approach is to find the globally best modeling method from a set of candidate methods.
MinP Score Tests with an Inequality Constrained Parameter Space
We show that our tests perform better than/or perform as good as existing score tests in terms of joint testing, and has furthermore the added benefit of allowing for simultaneously testing individual elements of parameter of interest.
When Face Recognition Meets Occlusion: A New Benchmark
In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures, and colors) to achieve a large number of more realistic occlusion types.
To update or not to update? Delayed Nonparametric Bandits with Randomized Allocation
In randomized strategies, the extent of exploration-exploitation is controlled by a user-determined exploration probability sequence.
Is a Classification Procedure Good Enough? A Goodness-of-Fit Assessment Tool for Classification Learning
For testing parametric classification models, the BAGofT has a broader scope than the existing methods since it is not restricted to specific parametric models (e. g., logistic regression).
Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards
We study a multi-armed bandit problem with covariates in a setting where there is a possible delay in observing the rewards.
Model Selection Techniques -- An Overview
In the era of big data, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power.
Bridging AIC and BIC: a new criterion for autoregression
When the data is generated from a finite order autoregression, the Bayesian information criterion is known to be consistent, and so is the new criterion.
