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Found 34 papers, 8 papers with code
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement
Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS).
Graph Expansion in Pruned Recurrent Neural Network Layers Preserve Performance
We prune recurrent networks such as RNNs and LSTMs, maintaining a large spectral gap of the underlying graphs and ensuring their layerwise expansion properties.
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels
Machine learning, with its myriad applications, has become an integral component of numerous technological systems.
ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
We present a novel face swapping method using the progressively growing structure of a pre-trained StyleGAN.
Optimizing Odia Braille Literacy: The Influence of Speed on Error Reduction and Enhanced Comprehension
According to the study, there was a noticeable correlation between reading speed and reading errors.
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel
In this paper, as a separate case study, we demonstrate that a DL model secured with differential privacy (a popular countermeasure against MIA) is still vulnerable to MIA against an adversary exploiting Class Leakage.
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris.
Revisiting the Lottery Ticket Hypothesis: A Ramanujan Graph Perspective
Subsequently, with the loss of Ramanujan graph property, accuracy begins to reduce sharply.
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks
This means that an adversarial crafted image which is sufficiently close (visually indistinguishable) to its representative class can often be misclassified to be a member of a different class.
Multilingual Audio-Visual Smartphone Dataset And Evaluation
This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios.
Explaining Outcomes of Multi-Party Dialogues using Causal Learning
Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics.
Alternating Direction Method of Multipliers for Quantization
For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for Quantization ($\texttt{ADMM-Q}$) algorithm, which is a variant of the widely-used ADMM method applied to our discrete optimization problem.
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
In this paper, we present a texture aware lightweight deep learning framework for iris recognition.
ExTra: Transfer-guided Exploration
Given an optimal policy in a related task-environment, we show that its bisimulation distance from the current task-environment gives a lower bound on the optimal advantage of state-action pairs in the current task-environment.
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
In order to address these issues, we aim to develop a framework for material-agnostic information retrieval in hyperspectral images based on Positive-Unlabelled (PU) classification.
Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks
Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies.
SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification.
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Ultrasound image compression by preserving speckle-based key information is a challenging task.
Visual Attention for Behavioral Cloning in Autonomous Driving
The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map.
An LSTM-CRF Based Approach to Token-Level Metaphor Detection
In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF.
Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network
Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions.
Sentence Alignment using Unfolding Recursive Autoencoders
In this paper, we propose a novel two step algorithm for sentence alignment in monolingual corpora using Unfolding Recursive Autoencoders.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Deep learning based landcover classification algorithms have recently been proposed in literature.
Ranked #12 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
Recurrent Memory Addressing for describing videos
In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models.
WEPSAM: Weakly Pre-Learnt Saliency Model
Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets.
Visual saliency detection: a Kalman filter based approach
In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature.
Visualization Regularizers for Neural Network based Image Recognition
The success of deep neural networks is mostly due their ability to learn meaningful features from the data.
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images.
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
In this paper a synchronized parallel algorithm for pre-training deep networks on multi-core machines has been proposed.
A dense subgraph based algorithm for compact salient image region detection
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.
Trapezoidal Fuzzy Numbers for the Transportation Problem
The multivalued nature of Fuzzy Sets allows handling of uncertainty and vagueness involved in the cost values of each cells in the transportation table.
