221 research outputs found
Generative Noisy-Label Learning by Implicit Dicriminative Approximation with Partial Label Prior
The learning with noisy labels has been addressed with both discriminative
and generative models. Although discriminative models have dominated the field
due to their simpler modeling and more efficient computational training
processes, generative models offer a more effective means of disentangling
clean and noisy labels and improving the estimation of the label transition
matrix. However, generative approaches maximize the joint likelihood of noisy
labels and data using a complex formulation that only indirectly optimizes the
model of interest associating data and clean labels. Additionally, these
approaches rely on generative models that are challenging to train and tend to
use uninformative clean label priors. In this paper, we propose a new
generative noisy-label learning approach that addresses these three issues.
First, we propose a new model optimisation that directly associates data and
clean labels. Second, the generative model is implicitly estimated using a
discriminative model, eliminating the inefficient training of a generative
model. Third, we propose a new informative label prior inspired by partial
label learning as supervision signal for noisy label learning. Extensive
experiments on several noisy-label benchmarks demonstrate that our generative
model provides state-of-the-art results while maintaining a similar
computational complexity as discriminative models
Reinforcement Learning Based Efficiency Optimization Scheme for the DAB DC-DC Converter with Triple-Phase-Shift Modulation
Small-signal modelling of AC/MTDC hybrid power systems using Multi-Layer Component Connection Method
A Closer Look at Audio-Visual Semantic Segmentation
Audio-visual segmentation (AVS) is a complex task that involves accurately
segmenting the corresponding sounding object based on audio-visual queries.
Successful audio-visual learning requires two essential components: 1) an
unbiased dataset with high-quality pixel-level multi-class labels, and 2) a
model capable of effectively linking audio information with its corresponding
visual object. However, these two requirements are only partially addressed by
current methods, with training sets containing biased audio-visual data, and
models that generalise poorly beyond this biased training set. In this work, we
propose a new strategy to build cost-effective and relatively unbiased
audio-visual semantic segmentation benchmarks. Our strategy, called Visual
Post-production (VPO), explores the observation that it is not necessary to
have explicit audio-visual pairs extracted from single video sources to build
such benchmarks. We also refine the previously proposed AVSBench to transform
it into the audio-visual semantic segmentation benchmark AVSBench-Single+.
Furthermore, this paper introduces a new pixel-wise audio-visual contrastive
learning method to enable a better generalisation of the model beyond the
training set. We verify the validity of the VPO strategy by showing that
state-of-the-art (SOTA) models trained with datasets built by matching audio
and visual data from different sources or with datasets containing audio and
visual data from the same video source produce almost the same accuracy. Then,
using the proposed VPO benchmarks and AVSBench-Single+, we show that our method
produces more accurate audio-visual semantic segmentation than SOTA models.
Code and dataset will be available
A Deep Q-Network based optimized modulation scheme for Dual-Active-Bridge converter to reduce the RMS current
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Multi-modal learning focuses on training models by equally combining multiple
input data modalities during the prediction process. However, this equal
combination can be detrimental to the prediction accuracy because different
modalities are usually accompanied by varying levels of uncertainty. Using such
uncertainty to combine modalities has been studied by a couple of approaches,
but with limited success because these approaches are either designed to deal
with specific classification or segmentation problems and cannot be easily
translated into other tasks, or suffer from numerical instabilities. In this
paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates
uncertainty by measuring feature density via Cross-modal Random Network
Prediction (CRNP). CRNP is designed to require little adaptation to translate
between different prediction tasks, while having a stable training process.
From a technical point of view, CRNP is the first approach to explore random
network prediction to estimate uncertainty and to combine multi-modal data.
Experiments on two 3D multi-modal medical image segmentation tasks and three 2D
multi-modal computer vision classification tasks show the effectiveness,
adaptability and robustness of CRNP. Also, we provide an extensive discussion
on different fusion functions and visualization to validate the proposed model
Learning Support and Trivial Prototypes for Interpretable Image Classification
Prototypical part network (ProtoPNet) methods have been designed to achieve
interpretable classification by associating predictions with a set of training
prototypes, which we refer to as trivial prototypes because they are trained to
lie far from the classification boundary in the feature space. Note that it is
possible to make an analogy between ProtoPNet and support vector machine (SVM)
given that the classification from both methods relies on computing similarity
with a set of training points (i.e., trivial prototypes in ProtoPNet, and
support vectors in SVM). However, while trivial prototypes are located far from
the classification boundary, support vectors are located close to this
boundary, and we argue that this discrepancy with the well-established SVM
theory can result in ProtoPNet models with inferior classification accuracy. In
this paper, we aim to improve the classification of ProtoPNet with a new method
to learn support prototypes that lie near the classification boundary in the
feature space, as suggested by the SVM theory. In addition, we target the
improvement of classification results with a new model, named ST-ProtoPNet,
which exploits our support prototypes and the trivial prototypes to provide
more effective classification. Experimental results on CUB-200-2011, Stanford
Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves
state-of-the-art classification accuracy and interpretability results. We also
show that the proposed support prototypes tend to be better localised in the
object of interest rather than in the background region
- …