Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Digital processing of speech signals

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more realistic way. Our setup gets rid of too much prior knowledge on the test data, and assumes additional unknown acoustic events both in training and test data, which in practice have to be identified as background. Taking this into account during training, the robustness in real-world scenarios can be significantly increased, with an average event-based error rate reduction of an absolute 34 percentage points. Further we show and compare the performance of multi-event (polyphonic) classifiers vs. single-event classifiers while outlining the benefits of multi-event training.

Beyond the Dcase 2017 Challenge on Rare Sound Event Detection: A Proposal for a More Realistic Training and Test Framework

Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed by unsupervised domain adaptation (UDA) approaches trained either simultaneously on source and target domain datasets or even source-free only on target data in an offline fashion. In this work, we further expand a source-free UDA approach to a continual and therefore online-capable UDA on a single-image basis for semantic segmentation. Accordingly, our method only requires the pre-trained model from the supplier (trained in the source domain) and the current (unlabeled target domain) camera image. Our method Continual BatchNorm Adaptation (CBNA) modifies the source domain statistics in the batch normalization layers, using target domain images in an unsupervised fashion, which yields consistent performance improvements during inference. Thereby, in contrast to existing works, our approach can be applied to improve a DNN continuously on a single-image basis during deployment without access to source data, without algorithmic delay, and nearly without computational overhead. We show the consistent effectiveness of our method across a wide variety of source/target domain settings for semantic segmentation. Code is available at https://github.com/ifnspaml/CBNA

Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

For high-quality digital audio transmitted over error-prone short-range wireless channels robust source decoding with low decoding delay is desired. However, many previous approaches from the field of audio error concealment are solely intuitively motivated or introduce algorithmic delay. In contrast, this paper deals with a Bayesian framework for delayless full-band soft-decision error concealment for quantized but uncompressed audio utilizing only residual redundancy in the audio signal and channel reliability information. In principle, it can be applied to any channel providing reliability information. As a novelty, we employ multiple adaptively shaped prior probability distributions in the decoding process. These are utilized in conjunction with the autocorrelation method or the normalized least-mean-square (NLMS) algorithm in order to compute prediction probabilities within the Bayesian framework. Experiments carried out on representative audio data transmitted over additive white Gaussian noise (AWGN) channels show significant enhancements in audio quality.

Delayless soft-decision decoding of high-quality audio with adaptively shaped priors

The detection of tactical maneuvers performed by other traffic participants is a key component for modern autonomous driving systems. We present a novel vision-based approach to detect tactical maneuvers for vehicles, pedestrians, and bicycles. Our approach uses neural networks with temporal convolutions to incorporate temporal information into the detection. The training and evaluation of the presented architecture is performed on a video dataset obtained in a city and industrial environment spanning 3.5 hours. Our approach detects nine distinct maneuver classes with an average accuracy of 54.21%, with single detection accuracies of up to 88.17%.

Tim Fingscheidt

Papers

Beyond the Dcase 2017 Challenge on Rare Sound Event Detection: A Proposal for a More Realistic Training and Test Framework

Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

Delayless soft-decision decoding of high-quality audio with adaptively shaped priors

Towards Tactical Maneuver Detection for Autonomous Driving Based on Vision Only

A New Evaluation Methodology for Speech Emotion Recognition With Confidence Output