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A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
Traditionally, the main focus of image super-resolution techniques is on...
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Persistent Mixture Model Networks for Few-Shot Image Classification
We introduce Persistent Mixture Model (PMM) networks for representation ...
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Beyond ℋ-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
We reveal the incoherence between the widely-adopted empirical domain ad...
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Self-supervised Robust Object Detectors from Partially Labelled datasets
In the object detection task, merging various datasets from similar cont...
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Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
We aim at demonstrating the influence of diversity in the ensemble of CN...
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Input Dropout for Spatially Aligned Modalities
Computer vision datasets containing multiple modalities such as color, d...
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Associative Alignment for Few-shot Image Classification
Few-shot image classification aims at training a model by using only a f...
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A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
The uncertainty estimation is critical in real-world decision making app...
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Deep Active Learning: Unified and Principled Method for Query and Training
In this paper, we proposed a unified and principled method for both quer...
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Deep Parametric Indoor Lighting Estimation
We present a method to estimate lighting from a single image of an indoo...
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Toward Metrics for Differentiating Out-of-Distribution Sets
Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-o...
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Unsupervised Temperature Scaling: Post-Processing Unsupervised Calibration of Deep Models Decisions
Great performances of deep learning are undeniable, with impressive resu...
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A Principled Approach for Learning Task Similarity in Multitask Learning
Multitask learning aims at solving a set of related tasks simultaneously...
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Learning of Image Dehazing Models for Segmentation Tasks
To evaluate their performance, existing dehazing approaches generally re...
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A New Loss Function for Temperature Scaling to have Better Calibrated Deep Networks
However Deep neural networks recently have achieved impressive results f...
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Accumulating Knowledge for Lifelong Online Learning
Lifelong learning can be viewed as a continuous transfer learning proced...
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Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Convolutional Neural Networks (CNNs) allowed improving the state-of-the-...
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Evaluating and Characterizing Incremental Learning from Non-Stationary Data
Incremental learning from non-stationary data poses special challenges t...
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Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Detection and rejection of adversarial examples in security sensitive an...
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Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
With super-resolution optical microscopy, it is now possible to observe ...
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Diversity regularization in deep ensembles
Calibrating the confidence of supervised learning models is important fo...
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Out-distribution training confers robustness to deep neural networks
The easiness at which adversarial instances can be generated in deep neu...
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Query Completion Using Bandits for Engines Aggregation
Assisting users by suggesting completed queries as they type is a common...
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Learning to Predict Indoor Illumination from a Single Image
We propose an automatic method to infer high dynamic range illumination ...
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Robustness to Adversarial Examples through an Ensemble of Specialists
We are proposing to use an ensemble of diverse specialists, where specia...
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Estimating Quality in Multi-Objective Bandits Optimization
Many real-world applications are characterized by a number of conflictin...
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Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks
The storage and computation requirements of Convolutional Neural Network...
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Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This paper proposes a self-adaptation mechanism to manage the resources ...
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