52,779 research outputs found
Co-training for Demographic Classification Using Deep Learning from Label Proportions
Deep learning algorithms have recently produced state-of-the-art accuracy in
many classification tasks, but this success is typically dependent on access to
many annotated training examples. For domains without such data, an attractive
alternative is to train models with light, or distant supervision. In this
paper, we introduce a deep neural network for the Learning from Label
Proportion (LLP) setting, in which the training data consist of bags of
unlabeled instances with associated label distributions for each bag. We
introduce a new regularization layer, Batch Averager, that can be appended to
the last layer of any deep neural network to convert it from supervised
learning to LLP. This layer can be implemented readily with existing deep
learning packages. To further support domains in which the data consist of two
conditionally independent feature views (e.g. image and text), we propose a
co-training algorithm that iteratively generates pseudo bags and refits the
deep LLP model to improve classification accuracy. We demonstrate our models on
demographic attribute classification (gender and race/ethnicity), which has
many applications in social media analysis, public health, and marketing. We
conduct experiments to predict demographics of Twitter users based on their
tweets and profile image, without requiring any user-level annotations for
training. We find that the deep LLP approach outperforms baselines for both
text and image features separately. Additionally, we find that co-training
algorithm improves image and text classification by 4% and 8% absolute F1,
respectively. Finally, an ensemble of text and image classifiers further
improves the absolute F1 measure by 4% on average
Catastrophic Overfitting: A Potential Blessing in Disguise
Fast Adversarial Training (FAT) has gained increasing attention within the
research community owing to its efficacy in improving adversarial robustness.
Particularly noteworthy is the challenge posed by catastrophic overfitting (CO)
in this field. Although existing FAT approaches have made strides in mitigating
CO, the ascent of adversarial robustness occurs with a non-negligible decline
in classification accuracy on clean samples. To tackle this issue, we initially
employ the feature activation differences between clean and adversarial
examples to analyze the underlying causes of CO. Intriguingly, our findings
reveal that CO can be attributed to the feature coverage induced by a few
specific pathways. By intentionally manipulating feature activation differences
in these pathways with well-designed regularization terms, we can effectively
mitigate and induce CO, providing further evidence for this observation.
Notably, models trained stably with these terms exhibit superior performance
compared to prior FAT work. On this basis, we harness CO to achieve `attack
obfuscation', aiming to bolster model performance. Consequently, the models
suffering from CO can attain optimal classification accuracy on both clean and
adversarial data when adding random noise to inputs during evaluation. We also
validate their robustness against transferred adversarial examples and the
necessity of inducing CO to improve robustness. Hence, CO may not be a problem
that has to be solved
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
This paper presents a general vector-valued reproducing kernel Hilbert spaces
(RKHS) framework for the problem of learning an unknown functional dependency
between a structured input space and a structured output space. Our formulation
encompasses both Vector-valued Manifold Regularization and Co-regularized
Multi-view Learning, providing in particular a unifying framework linking these
two important learning approaches. In the case of the least square loss
function, we provide a closed form solution, which is obtained by solving a
system of linear equations. In the case of Support Vector Machine (SVM)
classification, our formulation generalizes in particular both the binary
Laplacian SVM to the multi-class, multi-view settings and the multi-class
Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is
obtained by solving a single quadratic optimization problem, as in standard
SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results
obtained on the task of object recognition, using several challenging datasets,
demonstrate the competitiveness of our algorithms compared with other
state-of-the-art methods.Comment: 72 page
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
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