Tootfinder

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
Alireza Sedighi Moghaddam, Mohammad Reza Mohammadi
https://arxiv.org/abs/2509.02351

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
Labeled data is a fundamental component in training supervised deep learning models for computer vision tasks. However, the labeling process, especially for ordinal image classification where class boundaries are often ambiguous, is prone to error and noise. Such label noise can significantly degrade the performance and reliability of machine learning models. This paper addresses the problem of detecting and correcting label noise in ordinal image classification tasks. To this end, a novel data…

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Yuzheng Cai, Yanzhao Zhang, Dingkun Long, Mingxin Li, Pengjun Xie, Weiguo Zheng
https://arxiv.org/abs/2509.00520

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
Text reranking models are a crucial component in modern systems like Retrieval-Augmented Generation, tasked with selecting the most relevant documents prior to generation. However, current Large Language Models (LLMs) powered rerankers often face a fundamental trade-off. On one hand, Supervised Fine-Tuning based pointwise methods that frame relevance as a binary classification task lack the necessary scoring discrimination, particularly for those built on reasoning LLMs. On the other hand, appr…

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Yunfei Liu, Mingxuan Liu, Wupeng Xie, Xinzhu Liu, Wenxue Liu, Yangang Sun, Xin Qiu, Cui Yuan, Jinhai Li
https://arxiv.org/abs/2508.00274

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Automatic modulation classification (AMC) is a basic technology in intelligent wireless communication systems. It is important for tasks such as spectrum monitoring, cognitive radio, and secure communications. In recent years, deep learning methods have made great progress in AMC. However, mainstream methods still face two key problems. First, they often use time-frequency images instead of raw signals. This causes loss of key modulation features and reduces adaptability to different communicat…

Learning with Mandelbrot and Julia
V. R. Tjahjono, S. F. Feng, E. R. M. Putri, H. Susanto
https://arxiv.org/abs/2509.00903 https://arxiv.org/pdf/2509.00903…

Learning with Mandelbrot and Julia
Recent developments in applied mathematics increasingly employ machine learning (ML)-particularly supervised learning-to accelerate numerical computations, such as solving nonlinear partial differential equations. In this work, we extend such techniques to objects of a more theoretical nature: the classification and structural analysis of fractal sets. Focusing on the Mandelbrot and Julia sets as principal examples, we demonstrate that supervised learning methods-including Classification and Re…

Network Traffic Classification Using Self-Supervised Learning and Confident Learning
Ehsan Eslami, Walaa Hamouda
https://arxiv.org/abs/2509.23522 https://a…

Network Traffic Classification Using Self-Supervised Learning and Confident Learning
Network traffic classification (NTC) is vital for efficient network management, security, and performance optimization, particularly with 5G/6G technologies. Traditional methods, such as deep packet inspection (DPI) and port-based identification, struggle with the rise of encrypted traffic and dynamic port allocations. Supervised learning methods provide viable alternatives but rely on large labeled datasets, which are difficult to acquire given the diversity and volume of network traffic. Mean…

Clustering of Acoustic Environments with Variational Autoencoders for Hearing Devices
Luan Vin\'icius Fiorio, Ivana Nikoloska, Wim van Houtum, Ronald M. Aarts
https://arxiv.org/abs/2510.01940

Clustering of Acoustic Environments with Variational Autoencoders for Hearing Devices
Particularly in hearing devices, the environmental context is taken into account for audio processing, often through classification. Traditional acoustic environment classification relies on classical algorithms, which are unable to extract meaningful representations of high-dimensionality data, or on supervised learning, being limited by the availability of labels. Knowing that human-imposed labels do not always reflect the true structure of acoustic scenes, we explore the (unsupervised) clust…

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Walter Silima, Fangxia An, Mattia Vaccari, Eslam A. Hussein, S. Randriamampandry
https://arxiv.org/abs/2510.00969

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragal…

Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, Ren\'e Heinrich, Houtan Ghaffari, Lukas Miklautz, Ilyass Moummad, Bernhard Sick, Christoph Scholz
https://arxiv.org/abs/2509.24901

Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding quality: The $\texttt{cls}$-token discards crucial token information about dispersed, localized events in multi-label audio. This weakness is rooted in the mismatch between the pretraining objective (operating globally) and the downstream task …

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Siyamalan Manivannan
https://arxiv.org/abs/2508.11511 https://ar…

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Deep Learning has emerged as a promising approach for skin lesion analysis. However, existing methods mostly rely on fully supervised learning, requiring extensive labeled data, which is challenging and costly to obtain. To alleviate this annotation burden, this study introduces a novel semi-supervised deep learning approach that integrates ensemble learning with online knowledge distillation for enhanced skin lesion classification. Our methodology involves training an ensemble of convolutional…

GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
Min Zeng, Jinfei Sun, Xueyou Luo, Caiquan Liu, Shiqi Zhang, Li Xie, Xiaoxin Chen
https://arxiv.org/abs/2509.12108

GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to RL. To address efficiency-capability trade-off, we propose the Guess-Think-Answer (GTA) framework that combines the efficiency of SFT with the capability gains of RL in a unified traini…

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Manal Binkhonain, Reem Alfayaz
https://arxiv.org/abs/2509.13868

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Requirements classification assigns natural language requirements to predefined classes, such as functional and non functional. Accurate classification reduces risk and improves software quality. Most existing models rely on supervised learning, which needs large labeled data that are costly, slow to create, and domain dependent; they also generalize poorly and often require retraining for each task. This study tests whether prompt based large language models can reduce data needs. We benchmark…

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Suman Cha, Hyunjoong Kim
https://arxiv.org/abs/2509.11511 https://

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffe…

Learning kernels with quantum optical circuits
A. Mandilara, A. D. Papadopoulos, D. Syvridis
https://arxiv.org/abs/2509.12072 https://arxiv.org/pdf/2509.12…

Learning kernels with quantum optical circuits
Support Vector Machines (SVMs) are a cornerstone of supervised learning, widely used for data classification. A central component of their success lies in kernel functions, which enable efficient computation of inner products in high-dimensional feature spaces. Recent years have seen growing interest in leveraging quantum circuits --both qubit-based and quantum optical-- for computing kernel matrices, with ongoing research exploring potential quantum advantages. In this work, we investigate two…

Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation
Tanjim Islam Riju, Shuchismita Anwar, Saman Sarker Joy, Farig Sadeque, Swakkhar Shatabda
https://arxiv.org/abs/2508.13068

Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation
We propose a two-stage multimodal framework that enhances disease classification and region-aware radiology report generation from chest X-rays, leveraging the MIMIC-Eye dataset. In the first stage, we introduce a gaze-guided contrastive learning architecture for disease classification. It integrates visual features, clinical labels, bounding boxes, and radiologist eye-tracking signals and is equipped with a novel multi-term gaze-attention loss combining MSE, KL divergence, correlation, and cen…

Elemental Frequency-Based Supervised Classification Approach for the Search of Novel Topological Materials
Zodinpuia Ralte, Ramesh Kumar, Mukhtiyar Singh
https://arxiv.org/abs/2509.09978

Elemental Frequency-Based Supervised Classification Approach for the Search of Novel Topological Materials
The machine learning based approaches efficiently solves the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often requires lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for machine learning-driven topological material's classification that is independent of crystallographic symmetr…

Analysing the prevalence of tidal features in HSC-SSP using self-supervised representation learning
A. Desmons, S. Brough, F. Lanusse, L. Canepa, A. Khalid
https://arxiv.org/abs/2509.18274

Enhancing Speech Emotion Recognition with Multi-Task Learning and Dynamic Feature Fusion
Honghong Wang, Jing Deng, Fanqin Meng, Rong Zheng
https://arxiv.org/abs/2508.17878 https…

Enhancing Speech Emotion Recognition with Multi-Task Learning and Dynamic Feature Fusion
This study investigates fine-tuning self-supervised learn ing (SSL) models using multi-task learning (MTL) to enhance speech emotion recognition (SER). The framework simultane ously handles four related tasks: emotion recognition, gender recognition, speaker verification, and automatic speech recog nition. An innovative co-attention module is introduced to dy namically capture the interactions between features from the primary emotion classification task and auxiliary tasks, en abling con…

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
Jules Cauzinille, Marius Miron, Olivier Pietquin, Masato Hagiwara, Ricard Marxer, Arnaud Rey, Benoit Favre
https://arxiv.org/abs/2509.04166

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
Self-supervised speech models have demonstrated impressive performance in speech processing, but their effectiveness on non-speech data remains underexplored. We study the transfer learning capabilities of such models on bioacoustic detection and classification tasks. We show that models such as HuBERT, WavLM, and XEUS can generate rich latent representations of animal sounds across taxa. We analyze the models properties with linear probing on time-averaged representations. We then extend the a…

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Domenico De Cristofaro, Vincenzo Norman Vitale, Alessandro Vietti
https://arxiv.org/abs/2508.17914

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate pho…

Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning
Chaoqun Cui, Caiyan Jia
https://arxiv.org/abs/2508.07205 https://

Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning
Current rumor detection methods based on propagation structure learning predominately treat rumor detection as a class-balanced classification task on limited labeled data. However, real-world social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. To address the data scarcity and imbalance issues, we construct two large-scale conversation datasets from Weibo and Twitter and analyze the domain distributions. We find obvious differences betwee…

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
Reihaneh Karimi, Mahdiyar Mousavi-Sadr, Mohammad H. Zhoolideh Haghighi, Fatemeh S. Tabatabaei
https://arxiv.org/abs/2508.09689

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series photometric dataset from the Kepler Space Telescope, comprising 3,198 flux measurements across 5,074 stars. We investigate the performance of four supervised classification algorithms, namely Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Logisti…

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Christos Sgouropoulos, Christos Nikou, Stefanos Vlachos, Vasileios Theiou, Christos Foukanelis, Theodoros Giannakopoulos
https://arxiv.org/abs/2509.10074

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conducted in the image domain, few-shot learning in audio classification remains relatively underexplored. In this work, we investigate the effect of integrating supervised contrastive loss into prototypical few shot training for audio classification. In detail, we demonstrate that an…

A Semantics-Aware Hierarchical Self-Supervised Approach to Classification of Remote Sensing Images
Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone
https://arxiv.org/abs/2510.04916

A Semantics-Aware Hierarchical Self-Supervised Approach to Classification of Remote Sensing Images
Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies that represent the semantic relationships among classes. However, these hierarchies are frequently overlooked, and most approaches focus only on fine-grained classification schemes. In this paper, we present a novel Semantics-Aware Hierarchical Consensus (SAHC) method for learn…

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Grazia Sveva Ascione, Nicol\`o Tamagnone
https://arxiv.org/abs/2509.09303

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Classifying patents by their relevance to the UN Sustainable Development Goals (SDGs) is crucial for tracking how innovation addresses global challenges. However, the absence of a large, labeled dataset limits the use of supervised learning. Existing methods, such as keyword searches, transfer learning, and citation-based heuristics, lack scalability and generalizability. This paper frames patent-to-SDG classification as a weak supervision problem, using citations from patents to SDG-tagged sci…

Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech
Abhijit Sinha, Harishankar Kumar, Mohit Joshi, Hemant Kumar Kathania, Shrikanth Narayanan, Sudarsana Reddy Kadiri
https://arxiv.org/abs/2508.10332

Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech
Children's speech presents challenges for age and gender classification due to high variability in pitch, articulation, and developmental traits. While self-supervised learning (SSL) models perform well on adult speech tasks, their ability to encode speaker traits in children remains underexplored. This paper presents a detailed layer-wise analysis of four Wav2Vec2 variants using the PFSTAR and CMU Kids datasets. Results show that early layers (1-7) capture speaker-specific cues more effectivel…

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
https://arxiv.org/abs/2510.07191

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Self-supervised learning (SSL) has advanced visual representation learning, but its value in chest radiography, a high-volume imaging modality with fine-grained findings, remains unclear. Meta's DINOv3 extends earlier SSL models through Gram-anchored self-distillation. Whether these design choices improve transfer learning for chest radiography has not been systematically tested. We benchmarked DINOv3 against DINOv2 and ImageNet initialization across seven datasets (n>814,000). Two representati…

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer, Sumaiya Akter, Md Rakibul Hasan, Md. Golam Rabiul Alam
https://arxiv.org/abs/2510.10719

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from unlabeled data. The framework introduces a dual-path contrastive learning based architecture that simultaneously processes 1D waveforms and …

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon
https://arxiv.org/abs/2508.03520

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Supervised learning for empathy regression is challenged by noisy self-reported empathy scores. While many algorithms have been proposed for learning with noisy labels in textual classification problems, the regression counterpart is relatively under-explored. We propose UPLME, an uncertainty-aware probabilistic language modelling framework to capture label noise in the regression setting of empathy detection. UPLME includes a probabilistic language model that predicts both empathy score and he…

Multi Anatomy X-Ray Foundation Model
Nishank Singla, Krisztian Koos, Farzin Haddadpour, Amin Honarmandi Shandiz, Lovish Chum, Xiaojian Xu, Qing Jin, Erhan Bas
https://arxiv.org/abs/2509.12146

Multi Anatomy X-Ray Foundation Model
X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, …

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Xu-Liang Fan, Jie Li
https://arxiv.org/abs/2508.01596 https://arxiv.o…

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio sources are faint. Best et al. (2023) classified the radio sources in LoTSS Deep Fields DR1 through multiwavelength SED fitting. With the classification results of them, we perform a supervised machine learning to distinguish radio AGNs and radio SFGs. Aims…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Weilun Xu, Tianhao Dai, Oscar Goudet, Xiaoxuan Wang
https://arxiv.org/abs/2509.11474 https://…

Acoustic Overspecification in Electronic Dance Music Taxonomy
Electronic Dance Music (EDM) classification typically relies on industry-defined taxonomies with numerous subgenres, yet the acoustic basis for these distinctions remains unclear. Current approaches use supervised learning with prescribed genre labels, assuming their validity without systematic evaluation. In this paper, we propose an unsupervised approach to discover the natural acoustic structure of EDM independent of commercial labels. Our method combines novel tempogram-based features captu…

Noisy Label Refinement with Semantically Reliable Synthetic Images
Yingxuan Li, Jiafeng Mao, Yusuke Matsui
https://arxiv.org/abs/2509.04298 https://arxiv.o…

Noisy Label Refinement with Semantically Reliable Synthetic Images
Semantic noise in image classification datasets, where visually similar categories are frequently mislabeled, poses a significant challenge to conventional supervised learning approaches. In this paper, we explore the potential of using synthetic images generated by advanced text-to-image models to address this issue. Although these high-quality synthetic images come with reliable labels, their direct application in training is limited by domain gaps and diversity constraints. Unlike convention…