4,873 research outputs found
Alternating Back-Propagation for Generator Network
This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating back-propagation algorithm iterates the following two steps: (1)
Inferential back-propagation, which infers the latent factors by Langevin
dynamics or gradient descent. (2) Learning back-propagation, which updates the
parameters given the inferred latent factors by gradient descent. The gradient
computations in both steps are powered by back-propagation, and they share most
of their code in common. We show that the alternating back-propagation
algorithm can learn realistic generator models of natural images, video
sequences, and sounds. Moreover, it can also be used to learn from incomplete
or indirect training data
Flow-based Intrinsic Curiosity Module
In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences
on Artificial Intelligence), all rights reserved. The link is provided as
follows: https://www.ijcai.org/Proceedings/2020/28
Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes
Zero-shot classification of image scenes which can recognize the image scenes
that are not seen in the training stage holds great promise of lowering the
dependence on large numbers of labeled samples. To address the zero-shot image
scene classification, the cross-modal feature alignment methods have been
proposed in recent years. These methods mainly focus on matching the visual
features of each image scene with their corresponding semantic descriptors in
the latent space. Less attention has been paid to the contrastive relationships
between different image scenes and different semantic descriptors. In light of
the challenge of large intra-class difference and inter-class similarity among
image scenes and the potential noisy samples, these methods are susceptible to
the influence of the instances which are far from these of the same classes and
close to these of other classes. In this work, we propose a multi-level
cross-modal feature alignment method via contrastive learning for zero-shot
classification of remote sensing image scenes. While promoting the
single-instance level positive alignment between each image scene with their
corresponding semantic descriptors, the proposed method takes the
cross-instance contrastive relationships into consideration,and learns to keep
the visual and semantic features of different classes in the latent space apart
from each other. Extensive experiments have been done to evaluate the
performance of the proposed method. The results show that our proposed method
outperforms state of the art methods for zero-shot remote sensing image scene
classification. All the code and data are available at github
https://github.com/masuqiang/MCFA-Pytorc
Comparison of R-ketamine and rapastinel antidepressant effects in the social defeat stress model of depression
Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform
We apply the recently developed adaptive non-harmonic model based on the
wave-shape function, as well as the time-frequency analysis tool called
synchrosqueezing transform (SST) to model and analyze oscillatory physiological
signals. To demonstrate how the model and algorithm work, we apply them to
study the pulse wave signal. By extracting features called the spectral pulse
signature, {and} based on functional regression, we characterize the
hemodynamics from the radial pulse wave signals recorded by the
sphygmomanometer. Analysis results suggest the potential of the proposed signal
processing approach to extract health-related hemodynamics features
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