3,246 research outputs found
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
We propose a novel algorithm for the task of supervised discriminative
distance learning by nonlinearly embedding vectors into a low dimensional
Euclidean space. We work in the challenging setting where supervision is with
constraints on similar and dissimilar pairs while training. The proposed method
is derived by an approximate kernelization of a linear Mahalanobis-like
distance metric learning algorithm and can also be seen as a kernel neural
network. The number of model parameters and test time evaluation complexity of
the proposed method are O(dD) where D is the dimensionality of the input
features and d is the dimension of the projection space - this is in contrast
to the usual kernelization methods as, unlike them, the complexity does not
scale linearly with the number of training examples. We propose a stochastic
gradient based learning algorithm which makes the method scalable (w.r.t. the
number of training examples), while being nonlinear. We train the method with
up to half a million training pairs of 4096 dimensional CNN features. We give
empirical comparisons with relevant baselines on seven challenging datasets for
the task of low dimensional semantic category based image retrieval.Comment: ICCV 2015 preprin
The growing U.S. trade imbalance with China
Over the past decade, the United States has gone from enjoying a small trade surplus with China to grappling with an enormous deficit. Just to keep the gap from expanding in 1997, U.S. exports to China would need to grow at an extraordinary rate--four times as fast as Chinese exports to the United States. Despite recent gains and China's efforts at trade liberalization, growth on that order appears unlikely, and the deficit can be expected to widen in the near term.Balance of trade ; China
Free versus fair trade: the dumping issue
Trade liberalization has had little effect on the use of antidumping tariffs - tariffs imposed on imports judged by a government to be unfairly priced. As more countries resort to such tariffs, questions arise about the merits of this form of trade protection, particularly when other remedies are available to industries hurt by import competition.Tariff ; Competition ; Free trade
Disentangling Adversarial Robustness and Generalization
Obtaining deep networks that are robust against adversarial examples and
generalize well is an open problem. A recent hypothesis even states that both
robust and accurate models are impossible, i.e., adversarial robustness and
generalization are conflicting goals. In an effort to clarify the relationship
between robustness and generalization, we assume an underlying, low-dimensional
data manifold and show that: 1. regular adversarial examples leave the
manifold; 2. adversarial examples constrained to the manifold, i.e.,
on-manifold adversarial examples, exist; 3. on-manifold adversarial examples
are generalization errors, and on-manifold adversarial training boosts
generalization; 4. regular robustness and generalization are not necessarily
contradicting goals. These assumptions imply that both robust and accurate
models are possible. However, different models (architectures, training
strategies etc.) can exhibit different robustness and generalization
characteristics. To confirm our claims, we present extensive experiments on
synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and
CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201
Loss Functions for Top-k Error: Analysis and Insights
In order to push the performance on realistic computer vision tasks, the
number of classes in modern benchmark datasets has significantly increased in
recent years. This increase in the number of classes comes along with increased
ambiguity between the class labels, raising the question if top-1 error is the
right performance measure. In this paper, we provide an extensive comparison
and evaluation of established multiclass methods comparing their top-k
performance both from a practical as well as from a theoretical perspective.
Moreover, we introduce novel top-k loss functions as modifications of the
softmax and the multiclass SVM losses and provide efficient optimization
schemes for them. In the experiments, we compare on various datasets all of the
proposed and established methods for top-k error optimization. An interesting
insight of this paper is that the softmax loss yields competitive top-k
performance for all k simultaneously. For a specific top-k error, our new top-k
losses lead typically to further improvements while being faster to train than
the softmax.Comment: In Computer Vision and Pattern Recognition (CVPR), 201
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Importance of visual context in scene understanding tasks is well recognized
in the computer vision community. However, to what extent the computer vision
models for image classification and semantic segmentation are dependent on the
context to make their predictions is unclear. A model overly relying on context
will fail when encountering objects in context distributions different from
training data and hence it is important to identify these dependencies before
we can deploy the models in the real-world. We propose a method to quantify the
sensitivity of black-box vision models to visual context by editing images to
remove selected objects and measuring the response of the target models. We
apply this methodology on two tasks, image classification and semantic
segmentation, and discover undesirable dependency between objects and context,
for example that "sidewalk" segmentation relies heavily on "cars" being present
in the image. We propose an object removal based data augmentation solution to
mitigate this dependency and increase the robustness of classification and
segmentation models to contextual variations. Our experiments show that the
proposed data augmentation helps these models improve the performance in
out-of-context scenarios, while preserving the performance on regular data.Comment: 14 pages (12 figures
Learning Using Privileged Information: SVM+ and Weighted SVM
Prior knowledge can be used to improve predictive performance of learning
algorithms or reduce the amount of data required for training. The same goal is
pursued within the learning using privileged information paradigm which was
recently introduced by Vapnik et al. and is aimed at utilizing additional
information available only at training time -- a framework implemented by SVM+.
We relate the privileged information to importance weighting and show that the
prior knowledge expressible with privileged features can also be encoded by
weights associated with every training example. We show that a weighted SVM can
always replicate an SVM+ solution, while the converse is not true and we
construct a counterexample highlighting the limitations of SVM+. Finally, we
touch on the problem of choosing weights for weighted SVMs when privileged
features are not available.Comment: 18 pages, 8 figures; integrated reviewer comments, improved
typesettin
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