5,229 research outputs found
Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning
Separating audio mixtures into individual instrument tracks has been a long
standing challenging task. We introduce a novel weakly supervised audio source
separation approach based on deep adversarial learning. Specifically, our loss
function adopts the Wasserstein distance which directly measures the
distribution distance between the separated sources and the real sources for
each individual source. Moreover, a global regularization term is added to
fulfill the spectrum energy preservation property regardless separation. Unlike
state-of-the-art weakly supervised models which often involve deliberately
devised constraints or careful model selection, our approach need little prior
model specification on the data, and can be straightforwardly learned in an
end-to-end fashion. We show that the proposed method performs competitively on
public benchmark against state-of-the-art weakly supervised methods
Stereo Computation for a Single Mixture Image
This paper proposes an original problem of \emph{stereo computation from a
single mixture image}-- a challenging problem that had not been researched
before. The goal is to separate (\ie, unmix) a single mixture image into two
constitute image layers, such that the two layers form a left-right stereo
image pair, from which a valid disparity map can be recovered. This is a
severely illposed problem, from one input image one effectively aims to recover
three (\ie, left image, right image and a disparity map). In this work we give
a novel deep-learning based solution, by jointly solving the two subtasks of
image layer separation as well as stereo matching. Training our deep net is a
simple task, as it does not need to have disparity maps. Extensive experiments
demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201
Support Neighbor Loss for Person Re-Identification
Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201
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