Search Results for author:
Found 16 papers, 4 papers with code
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
Recent data-privacy laws have sparked interest in machine unlearning, which involves removing the effect of specific training samples from a learnt model as if they were never present in the original training dataset.
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks
Knowledge distillation which learns a lightweight student model by distilling knowledge from a cumbersome teacher model is an attractive approach for learning compact deep neural networks (DNNs).
Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing
First, to learn informative representations that can preserve both intra- and inter-modal similarities, we leverage the recent advances in estimating variational lower-bound of MI to maximize the MI between the binary representations and input features and between binary representations of different modalities.
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks
With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels.
Unsupervised Deep Cross-modality Spectral Hashing
This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval.
BTEL: A Binary Tree Encoding Approach for Visual Localization
Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques.
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
This global vector is then subjected to a hashing function to generate a binary hash code.
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Another approach explored in the field relies on an ad-hoc linearization (in terms of N) of the triplet loss that introduces class centroids, which must be optimized using the whole training set for each mini-batch - this means that a naive implementation of this approach has run-time complexity O(N^2).
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences
In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources.
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images.
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing
For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations.
Compact Hash Code Learning with Binary Deep Neural Network
This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization.
Selective Deep Convolutional Features for Image Retrieval
Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors.
Enhance Feature Discrimination for Unsupervised Hashing
We introduce a novel approach to improve unsupervised hashing.
