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Found 15 papers, 3 papers with code
Training Multilayer Perceptrons by Sampling with Quantum Annealers
This leads to a strategy to train MLPs with quantum annealers as a sampling engine.
A Hybrid Quantum-Classical Algorithm for Robust Fitting
While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics.
Mutual information neural estimation for unsupervised multi-modal registration of brain images
We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector
In fact, advanced EO satellites perform deep learning-based cloud detection on board the satellites and downlink only clear-sky data to save precious bandwidth.
Physical Adversarial Attacks on an Aerial Imagery Object Detector
In this work, we demonstrate one of the first efforts on physical adversarial attacks on aerial imagery, whereby adversarial patches were optimised, fabricated and installed on or near target objects (cars) to significantly reduce the efficacy of an object detector applied on overhead images.
Quantum Annealing Formulation for Binary Neural Networks
Quantum annealing is a promising paradigm for building practical quantum computers.
A Chaos Theory Approach to Understand Neural Network Optimization
Then, we empirically show that for a large range of learning rates, SGD traverses the loss landscape across regions with largest eigenvalue of the Hessian similar to the inverse of the learning rate.
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space
Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes.
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces
In the context of GZSL, semantic information is non-visual data such as a text description of both seen and unseen classes.
Real-time tracker with fast recovery from target loss
When the confidence is low, we avoid updating the object's position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch's low-level visual content is used to swiftly update the object's position, before recovering from the target loss in the next frame.
Multi-modal Ensemble Classification for Generalized Zero Shot Learning
In this paper, we mitigate these issues by proposing a new GZSL method based on multi-modal training and testing processes, where the optimization explicitly promotes a balanced classification accuracy between seen and unseen classes.
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations.
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes.
Hide and Seek tracker: Real-time recovery from target loss
In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the target loss in the next frame.
FineTag: Multi-attribute Classification at Fine-grained Level in Images
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem.
