The study adapts a convolutional neural network to classify galaxy morphologies using crowd-sourced annotations from Galaxy Zoo 1. It evaluates how training strategies—such as training all layers versus only the last, incorporating hierarchical labels, varying data volume and annotator agreement, staged transfer learning, and ensembling—affect accuracy and efficiency. Results show that full-network training and high annotator agreement yield over 99% accuracy, while hierarchical approaches and staged learning help when data are limited.
arXiv:2608.30789v1 Announce Type: new Abstract: Supervised deep learning methods enable the rapid processing of ecological image data, but depend on a costly annotation process. Consequently, trainin...
arXiv:2602. 13298v4 Announce Type: replace-cross Abstract: This paper presents a controlled comparative study of convolutional neural network (CNN) topology and image classification performance across the architectural families VGG, ResNet, and GoogLeNet, evaluated on CIFAR-10 under a unified training protocol.
The paper introduces HAND, a biologically-inspired activation function that incorporates homeostasis, accelerating nonlinearity, and divisive normalization to act as an inductive bias in deep neural networks. Experiments on image classification show that using HAND allows a ConvNeXt-tiny model to reach ImageNet1k accuracy in 25 epochs versus 200 epochs for the baseline, and yields larger accuracy gains on long-tailed and reduced-data settings. The authors report that HAND does not degrade generalisation on common corruptions and can improve the model’s ability to reject unknown classes, with benefits observed across multiple CNN architectures and datasets.
arXiv:2610.28059v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) promise predictions that humans can inspect and correct through concepts such as "curved beak". Yet on CUB-200-2011,...
arXiv:2610.32573v1 Announce Type: new Abstract: Camera-trap species classification is dominated by large models from well-resourced organisations, which often lack the species-level distinctions regi...
The paper presents a probabilistic machine‑learning framework for classifying low‑ and high‑resolution stellar and extragalactic spectra, targeting the upcoming 4MOST survey. Four approaches were evaluated—CNNs, Dirichlet distribution, Monte Carlo dropout (MCD), and Bayesian neural networks with variational inference—using SDSS data and a 4MOST mock dataset. The MCD‑augmented CNN achieved the highest accuracies (92.6% on SDSS, 93.9% on mock data) while also delivering well‑calibrated uncertainty estimates with minimal extra computational cost.
The paper investigates how evaluation protocols and ground‑truth quality affect the classification performance of Multimodal Large Language Models (MLLMs). It identifies and corrects key issues such as discarded out‑of‑list outputs, inflated distractor choices, and poor open‑world mapping, and shows that design choices like batch size, image ordering, and text encoder selection significantly influence accuracy. Using a multilabel reannotation of 625 ImageNet‑1k classes (ReGT), the study finds that corrected labels can boost MLLM performance by up to 10.8%, narrowing the gap with supervised models, and demonstrates that MLLMs can assist human annotators in about half of difficult cases.
arXiv:2609.23561v1 Announce Type: new Abstract: Deploying efficient neural networks is essential in resource-constrained environments, yet compact models often sacrifice interpretability - a critical...
The study benchmarks Vision Transformers (ViTs) against convolutional neural networks (CNNs) for fine‑grained orchid genus identification in New Guinea’s species‑rich, data‑poor flora. Using a two‑stage system that first predicts genus and then retrieves similar species images, the authors fine‑tuned four pretrained backbones on 16,701 photographs from 120 genera and 1,350 species. The self‑supervised ViT DINOv2 achieved the highest genus accuracy (macro top‑1 66.9 %) and outperformed both CNNs and a domain‑matched pretrained ViT, demonstrating strong species retrieval and open‑set detection capabilities.
arXiv:2610.41841v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly capable of understanding images and performing complex visual reasoning, suggesting the possi...
arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?