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Found 12 papers, 3 papers with code
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples
Knowledge Distillation (KD) facilitates the transfer of discriminative capabilities from an advanced teacher model to a simpler student model, ensuring performance enhancement without compromising accuracy.
SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion
This study presents a deep learning-based approach to seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes.
Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition
In this paper, we propose an isolated sign language recognition model based on a model trained using Motion History Images (MHI) that are generated from RGB video frames.
Towards disease-aware image editing of chest X-rays
Disease-aware image editing by means of generative adversarial networks (GANs) constitutes a promising avenue for advancing the use of AI in the healthcare sector.
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research
However, several open challenges still need to be solved to allow SLR to be useful in practice.
A Hierarchical Approach to Remote Sensing Scene Classification
The results of our experiments show that although individual classifiers for different sub-categories in the hierarchical scheme perform considerably well, the accumulation of the classification errors in the cascaded structure prevents its classification performance from exceeding that of the non-hierarchical deep model
Iterative Facial Image Inpainting Based on an Encoder-Generator Architecture
Most of these approaches use encoder-decoder architectures and have different limitations such as allowing unique results for a given image and a particular mask.
AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
In AUTSL random train-test splits, our models performed up to 95. 95% accuracy.
Ranked #7 on
Sign Language Recognition
on AUTSL
Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition
Therefore, our analysis with deep features show that HMMs could also be utilized as well as deep sequence models in challenging isolated sign recognition problem.
Semi-supervised Image Attribute Editing using Generative Adversarial Networks
The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute.
Learning Multi-scale Features for Foreground Segmentation
Foreground segmentation algorithms aim segmenting moving objects from the background in a robust way under various challenging scenarios.
Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
Our network takes an RGB image in three different scales and produces a foreground segmentation probability mask for the corresponding image.
