Search Results for author:
Found 22 papers, 6 papers with code
EcoVal: An Efficient Data Valuation Framework for Machine Learning
In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner.
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation
Our work takes a novel approach to address these challenges in graph unlearning through knowledge distillation, as it distills to delete in GNN (D2DGN).
Efficient Neural Architecture Search for Emotion Recognition
In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition.
Deep Regression Unlearning
In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models.
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns.
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch.
Zero-Shot Machine Unlearning
In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models.
Fast Yet Effective Machine Unlearning
In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs
To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods.
Learning to Enhance Visual Quality via Hyperspectral Domain Mapping
We incorporate a self-supervision and a spectral profile regularization network to infer a plausible HSI from an RGB image.
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement
Low light conditions in aerial images adversely affect the performance of several vision based applications.
Domain-Aware Unsupervised Hyperspectral Reconstruction for Aerial Image Dehazing
The performance of SkyGAN is evaluated on the recent SateHaze1k dataset as well as the HAI dataset.
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos
Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos.
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
MotionRec: A Unified Deep Framework for Moving Object Recognition
To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i. e. MOR in a single-stage deep learning framework.
3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection
In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection.
3D CNN with Localized Residual Connections for Hyperspectral Image Classification
Our work chalks a comparative study with the existing methods employed for abstracting deeper features and propose a model which incorporates residual features from multiple stages in the network.
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
Detection of small-sized targets is of paramount importance in many aerial vision-based applications.
AVDNet: A Small-Sized Vehicle Detection Network for Aerial Visual Data
Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances.
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly.
Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval
A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript.
CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
Background subtraction in video provides the preliminary information which is essential for many computer vision applications.
