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Found 25 papers, 17 papers with code
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees.
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers
Furthermore, we will show that transformers trained on Audioset can be extremely effective representation extractors for a wide range of downstream tasks.
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them.
Few-Shot Learning by Dimensionality Reduction in Gradient Space
We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.
History Compression via Language Models in Reinforcement Learning
We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency.
The balancing principle for parameter choice in distance-regularized domain adaptation
Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems.
A Dataset Perspective on Offline Reinforcement Learning
The dataset characteristics are determined by the behavioral policy that samples this dataset.
Efficient Training of Audio Transformers with Patchout
However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity.
Ranked #3 on
Audio Classification
on FSD50K
(using extra training data)
Over-Parameterization and Generalization in Audio Classification
Convolutional Neural Networks (CNNs) have been dominating classification tasks in various domains, such as machine vision, machine listening, and natural language processing.
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks
As state-of-the-art CNN architectures-in computer vision and other domains-tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks.
Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning
In Contextual Reinforcement Learning (CRL), a change in the context variable can cause a change in the distribution of the states.
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
Deep Neural Networks are known to be very demanding in terms of computing and memory requirements.
Receptive-Field Regularized CNNs for Music Classification and Tagging
However, the MIR field is still dominated by the classical VGG-based CNN architecture variants, often in combination with more complex modules such as attention, and/or techniques such as pre-training on large datasets.
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models.
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels.
Receptive-field-regularized CNN variants for acoustic scene classification
One side effect of restricting the RF of CNNs is that more frequency information is lost.
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning.
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.
On Conditioning GANs to Hierarchical Ontologies
Additionally, we show that the O-GAN achieves better conditioning results evaluated by implicit similarity between the text and the generated image.
Mixture Density Generative Adversarial Networks
Generative Adversarial Networks have surprising ability for generating sharp and realistic images, though they are known to suffer from the so-called mode collapse problem.
Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data
We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models.
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning
To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN's representation, improving performance on unseen test data from a shifted distribution.
Probabilistic Generative Adversarial Networks
The central idea is to integrate a probabilistic model (a Gaussian Mixture Model, in our case) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at the same time gives a meaningful measure of the quality of the outputs generated by the network.
Likelihood Estimation for Generative Adversarial Networks
We present a simple method for assessing the quality of generated images in Generative Adversarial Networks (GANs).
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique.
