865 research outputs found
Towards Optimal Discrete Online Hashing with Balanced Similarity
When facing large-scale image datasets, online hashing serves as a promising
solution for online retrieval and prediction tasks. It encodes the online
streaming data into compact binary codes, and simultaneously updates the hash
functions to renew codes of the existing dataset. To this end, the existing
methods update hash functions solely based on the new data batch, without
investigating the correlation between such new data and the existing dataset.
In addition, existing works update the hash functions using a relaxation
process in its corresponding approximated continuous space. And it remains as
an open problem to directly apply discrete optimizations in online hashing. In
this paper, we propose a novel supervised online hashing method, termed
Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above
problems in a unified framework. BSODH employs a well-designed hashing
algorithm to preserve the similarity between the streaming data and the
existing dataset via an asymmetric graph regularization. We further identify
the "data-imbalance" problem brought by the constructed asymmetric graph, which
restricts the application of discrete optimization in our problem. Therefore, a
novel balanced similarity is further proposed, which uses two equilibrium
factors to balance the similar and dissimilar weights and eventually enables
the usage of discrete optimizations. Extensive experiments conducted on three
widely-used benchmarks demonstrate the advantages of the proposed method over
the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc
Power spectrum with growth for primordial black holes
The decrease of both the rolling speed of the inflaton and the sound speed of
the curvature perturbations can amplify the curvature perturbations during
inflation so as to generate a sizable amount of primordial black holes. In the
ultraslow-roll inflation scenario, it has been found that the power spectrum of
curvature perturbations has a growth. In this paper, we find that when
the speed of sound decreases suddenly, the curvature perturbations becomes
scale dependent in the infrared limit and the power spectrum of the curvature
perturbation only has a growth. Furthermore, by studying the evolution of
the power spectrum in the inflation model, in which both the sound speed of the
curvature perturbations and the rolling speed of the inflaton are reduced, we
find that the power spectrum is nearly scale invariant at the large scales to
satisfy the constraint from the cosmic microwave background radiation
observations, and at the same time can be enhanced at the small scales to
result in an abundant formation of primordial black holes. In the cases of the
simultaneous changes of the sound speed and the slow-roll parameter and
the change of the sound speed preceding that of the slow-roll parameter ,
the power spectrum can possess a growth under certain conditions, which
is the steepest growth of the power spectrum reported so far.Comment: 29 pages, 14 figures, to appear in PR
Growth of power spectrum due to decrease of sound speed during inflation
We study the amplification of the curvature perturbations due to a small
sound speed and find that its origin is different completely from that due to
the ultraslow-roll inflation. This is because when the sound speed is very
small the enhancement of the power spectrum comes from the fact that the
curvature perturbations at the scales smaller than the cosmic microwave
background (CMB) scale becomes scale-variant, rather than growing that leads to
the amplification of the curvature perturbations during the ultraslow-roll
inflation. At large scales the power spectrum of the curvature perturbations
remains to be scale invariant, which is consistent with the CMB observations,
and then it will have a transient growth and finally approach a
growth as the scale becomes smaller and smaller. Thus the power spectrum can be
enhanced to generate a sizable amount of primordial black holes. Furthermore,
when the high order correction in the dispersion relation of the curvature
perturbations is considered the growth of the power spectrum of the curvature
perturbations has the same origin as that in the case without this correction.Comment: 11 pages, 1 figure. three references adde
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in
recent years, the common occlusion case caused by different obstacles is still
an unsettled issue in real application scenarios. Existing methods mainly
address this issue by employing body clues provided by an extra network to
distinguish the visible part. Nevertheless, the inevitable domain gap between
the assistant model and the ReID datasets has highly increased the difficulty
to obtain an effective and efficient model. To escape from the extra
pre-trained networks and achieve an automatic alignment in an end-to-end
trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two
self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask
Generator which utilizes the hierarchical semantic to select the visible
pattern space between the high-quality holistic prototype and the feature
representation of the occluded input image. Under this condition, the occluded
representation could be well aligned in a selected subspace spontaneously.
Then, to enrich the feature representation of the high-quality holistic
prototype and provide a more complete feature space, we introduce a Head Enrich
Module to encourage different heads to aggregate different patterns
representation in the whole image. Extensive experimental evaluations conducted
on occluded and holistic person re-identification benchmarks demonstrate the
superior performance of the DPM over the state-of-the-art methods. The code is
released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202
CAT:Collaborative Adversarial Training
Adversarial training can improve the robustness of neural networks. Previous
methods focus on a single adversarial training strategy and do not consider the
model property trained by different strategies. By revisiting the previous
methods, we find different adversarial training methods have distinct
robustness for sample instances. For example, a sample instance can be
correctly classified by a model trained using standard adversarial training
(AT) but not by a model trained using TRADES, and vice versa. Based on this
observation, we propose a collaborative adversarial training framework to
improve the robustness of neural networks. Specifically, we use different
adversarial training methods to train robust models and let models interact
with their knowledge during the training process. Collaborative Adversarial
Training (CAT) can improve both robustness and accuracy. Extensive experiments
on various networks and datasets validate the effectiveness of our method. CAT
achieves state-of-the-art adversarial robustness without using any additional
data on CIFAR-10 under the Auto-Attack benchmark. Code is available at
https://github.com/liuxingbin/CAT.Comment: Tech repor
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