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Found 14 papers, 9 papers with code
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
Unsupervised image classification is a challenging computer vision task.
Ranked #1 on
Image Clustering
on STL-10
(Backbone metric, using extra
training data)
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks.
Self Supervised Vision for Climate Downscaling
The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research.
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
Previous research has demonstrated the effectiveness of deep learning for downscaling tasks.
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other.
FedDefender: Client-Side Attack-Tolerant Federated Learning
Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework.
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection
Knowledge of the changing traffic is critical in risk management.
Classification of Goods Using Text Descriptions With Sentences Retrieval
The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office.
Elsa: Energy-based learning for semi-supervised anomaly detection
Anomaly detection aims at identifying deviant instances from the normal data distribution.
Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
Ranked #1 on
Image Clustering
on CIFAR-100
(Train Set metric, using extra
training data)
Active Learning for Human-in-the-Loop Customs Inspection
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected.
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.
Lightweight and Robust Representation of Economic Scales from Satellite Imagery
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas.
