1,408 research outputs found
Do We Train on Test Data? Purging CIFAR of Near-Duplicates
The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked
datasets in computer vision and are often used to evaluate novel methods and
model architectures in the field of deep learning. However, we find that 3.3%
and 10% of the images from the test sets of these datasets have duplicates in
the training set. These duplicates are easily recognizable by memorization and
may, hence, bias the comparison of image recognition techniques regarding their
generalization capability. To eliminate this bias, we provide the "fair CIFAR"
(ciFAIR) dataset, where we replaced all duplicates in the test sets with new
images sampled from the same domain. We then re-evaluate the classification
performance of various popular state-of-the-art CNN architectures on these new
test sets to investigate whether recent research has overfitted to memorizing
data instead of learning abstract concepts. We find a significant drop in
classification accuracy of between 9% and 14% relative to the original
performance on the duplicate-free test set. The ciFAIR dataset and pre-trained
models are available at https://cvjena.github.io/cifair/, where we also
maintain a leaderboard.Comment: Journal of Imagin
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Query images presented to content-based image retrieval systems often have
various different interpretations, making it difficult to identify the search
objective pursued by the user. We propose a technique for overcoming this
ambiguity, while keeping the amount of required user interaction at a minimum.
To achieve this, the neighborhood of the query image is divided into coherent
clusters from which the user may choose the relevant ones. A novel feedback
integration technique is then employed to re-rank the entire database with
regard to both the user feedback and the original query. We evaluate our
approach on the publicly available MIRFLICKR-25K dataset, where it leads to a
relative improvement of average precision by 23% over the baseline retrieval,
which does not distinguish between different image senses.Comment: VISAPP 2018 paper, 8 pages, 5 figures. Source code:
https://github.com/cvjena/ai
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Self-Selection into Teaching: The Role of Teacher Education Institutions
Good teachers are critical for a high-quality educational system. This in turns leads to the question of who is interested in going into the teaching profession. Although research has been done on the professional careers of teachers, the issue of self-selection into teacher education has been mostly overlooked until now. The analyses contained in our study are based on a representative sampling of over 1500 high-school students in Switzerland shortly before graduation. The findings indicate that there is a self-selection process with regard to courses of study at teaching training institutions, which is reinforced by institutional and structural characteristics of the types of higher education institutions and the courses of study they offer. This can clearly be seen in comparison with high-school students preparing to study at another type of higher educational institution (university). Accordingly, the findings of this paper tend to indicate that the choices made by future teachers depend to a large extent also on where and how teachers are trained.teacher education, teacher training, teacher education colleges, self-selection, v
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time
budgets during application. They allow for individual budgets given a priori
for each test example and for anytime prediction, i.e., a possible interruption
at multiple stages during inference while still providing output estimates. Our
approach can therefore tackle the computational costs and energy demands of
DNNs in an adaptive manner, a property essential for real-time applications.
Our Impatient DNNs are based on a new general framework of learning dynamic
budget predictors using risk minimization, which can be applied to current DNN
architectures by adding early prediction and additional loss layers. A key
aspect of our method is that all of the intermediate predictors are learned
jointly. In experiments, we evaluate our approach for different budget
distributions, architectures, and datasets. Our results show a significant gain
in expected accuracy compared to common baselines.Comment: British Machine Vision Conference (BMVC) 201
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