Search Results for author:
Found 26 papers, 14 papers with code
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts.
CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems
Therefore, adapting the FC-AE-GPR model to a new scenario requires only retraining the GPR model with a small training dataset.
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
In this regard, source-free domain adaptation (SFDA) excels as access to source data is no longer required during adaptation.
Ranked #5 on
Source-Free Domain Adaptation
on VisDA-2017
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples.
CoDGraD: A Code-based Distributed Gradient Descent Scheme for Decentralized Convex Optimization
In this paper, we consider a large network containing many regions such that each region is equipped with a worker with some data processing and communication capability.
RF Signal Transformation and Classification using Deep Neural Networks
Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets.
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
The method proposed in this work referred to as RODD outperforms SOTA detection performance on an extensive suite of benchmark datasets on OOD detection tasks.
Ranked #1 on
Out-of-Distribution Detection
on cifar100
(using extra training data)
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data.
MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
On the other hand, SoA ray tracing has an average error of 1. 0 m and 2. 2 m, respectively, but requires explicit AoD and ToA information to perform the localization task.
Spectrum Shaping For Multiple Link Discovery in 6G THz Systems
This paper presents a novel antenna configuration to measure directions of multiple signal sources at the receiver in a THz mobile network via a single channel measurement.
Phase Spectrometry For High Precision mm-Wave DoA Estimation In 5G Systems
Moreover, our results also show that, the angular resolution of PS depends on the distance between the two antennas and the band-width of the frequency code-book.
SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network
In this paper, we propose a generative adversarial network-based reconstruction framework for single-pixel imaging, referred to as SPI-GAN.
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
The proposed scheme, referred to as RL-NCS, aims to boost the performance of signal recovery through an optimal and adaptive distribution of sensing energy among two groups of coefficients of the signal, referred to as the region of interest (ROI) coefficients and non-ROI coefficients.
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
The problem of simultaneous column and row subset selection is addressed in this paper.
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model).
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks
This paper presents a data-driven localization framework with high precision in time-varying complex multipath environments, such as dense urban areas and indoors, where GPS and model-based localization techniques come short.
Face Image Retrieval With Attribute Manipulation
For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations
We also propose an anomaly detection method to identify the target class in a Trojaned network.
Ranked #1 on
Adversarial Defense
on TrojAI Round 1
Odyssey: Creation, Analysis and Detection of Trojan Models
We propose a detector that is based on the analysis of the intrinsic DNN properties; that are affected due to the Trojaning process.
Subspace Capsule Network
In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appearance or implicitly defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules.
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks
A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed.
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples.
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?
We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective.
Union of Low-Rank Subspaces Detector
The problem of signal detection using a flexible and general model is considered.
