Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation

no code implementations • 30 Dec 2023 • Sebastian-Vasile Echim, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel, Florin Pop

Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests.

Adversarial Attack Classification +4

From Fake to Hyperpartisan News Detection Using Domain Adaptation

no code implementations • 4 Aug 2023 • Răzvan-Alexandru Smădu, Sebastian-Vasile Echim, Dumitru-Clementin Cercel, Iuliana Marin, Florin Pop

In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection.

Clustering Contrastive Learning +5

Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis

no code implementations • 13 Jun 2023 • Sebastian-Vasile Echim, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop

Satire detection and sentiment analysis are intensively explored natural language processing (NLP) tasks that study the identification of the satirical tone from texts and extracting sentiments in relationship with their targets.

Satire Detection Sentiment Analysis