9 papers
Brain-Inspired Stochastic Joint Embedding Representation Learning
Makoto Yamada, Kian Ming A. Chai, Ayoub Rhim +3
Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionized computer vision. However, thes…
What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clustering
Amir Hossein Saleknia, Mohammad Sabokrou
In computer vision, a prevailing method for quantifying dataset bias is to train a model to distinguish between datasets. High classification accuracy is then interpreted as eviden…
TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
Alireza Salehi, Ehsan Karami, Sepehr Noey +4
Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) lever…
Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
Mohammad Hadi Foroughi, Seyed Hamed Rastegar, Mohammad Sabokrou +1
Federated learning (FL) enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as a promising solution for c…
APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
Sasan Sharifipour, Constantino Ãlvarez Casado, Mohammad Sabokrou +1
Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predi…
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection
Alireza Salehi, Mohammadreza Salehi, Reshad Hosseini +3
Zero-shot anomaly detection and localization aims to learn from source-domain data and generalize to unseen target domains without target-domain samples. Recent CLIP-based methods…