Search Results for author:
Found 7 papers, 2 papers with code
Deep Active Learning Using Barlow Twins
In this paper, we unify these two families of approaches from the angle of active learning using self-supervised learning mainfold and propose Deep Active Learning using BarlowTwins(DALBT), an active learning method for all the datasets using combination of classifier trained along with self-supervised loss framework of Barlow Twins to a setting where the model can encode the invariance of artificially created distortions, e. g. rotation, solarization, cropping etc.
Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing
Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems.
Domain Agnostic Few-Shot Learning For Document Intelligence
In this work, we address the problem of few-shot document image classification under domain shift.
Efficient Document Image Classification Using Region-Based Graph Neural Network
Recent advancements in large pre-trained computer vision and language models and graph neural networks has lent document image classification many tools.
Ranked #1 on
Document Image Classification
on Tobacco-3482
(Memory metric)
Continual Learning with Deep Artificial Neurons
We demonstrate that it is possible to meta-learn a single parameter vector, which we dub a neuronal phenotype, shared by all DANs in the network, which facilitates a meta-objective during deployment.
Deep Active Learning via Open Set Recognition
The goal of active learning is to infer the informativeness of unlabeled samples so as to minimize the number of requests to the oracle.
Self-Net: Lifelong Learning via Continual Self-Modeling
We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights.
