arXiv:2606. 14886v1 Announce Type: cross Abstract: In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image classification task.
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
arXiv:2609.13024v1 Announce Type: new Abstract: As a key model compression technique, knowledge distillation aims to transfer knowledge from a high-capacity teacher model to a lightweight student mod...
TinyCNN is a lightweight convolutional neural network with only 193,190 trainable parameters designed for on‑device plant disease classification. It achieves 98.88% test accuracy on the 38‑class PlantVillage benchmark, outperforming larger models while consuming far less energy, memory, and cost. The study also explores knowledge distillation to further compress the model and evaluates cross‑dataset robustness, finding a significant performance drop when moving from PlantVillage to PlantDoc due to background‑driven shortcut learning.
Vision Transformers underperform convolutional networks when training data is scarce, and distilling convolutional inductive biases from a CNN teacher is an effective remedy that leaves the deployed model unchanged. General-purpose feature distillation, however, transfers little in this setting.
The paper compares classical machine learning algorithms—such as Logistic Regression, SVM, Random Forest, XGBoost, and CatBoost—with Tabular Deep Learning models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs) for urban land cover classification using a UCI dataset derived from high‑resolution aerial imagery. It evaluates performance across nine land cover classes, addressing challenges like high dimensionality, heterogeneous features, and class imbalance by applying weighted cross‑entropy loss for deep models and measuring accuracy, macro‑precision, macro‑recall, macro‑F1, AUC‑ROC, and confusion matrices. Results indicate that while tree ensembles remain strong baselines, Tabular Deep Learning can match or surpass them when non‑linear interactions are prominent and imbalance handling is effective.
arXiv:2606. 14684v1 Announce Type: cross Abstract: Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware.
The paper presents a framework that enhances deep‑learning tree‑cover mapping in New South Wales by fusing multiple imagery sources and normalizing image quality. It introduces an image‑composition technique that removes defects and a prediction‑fusion method that reduces reliance on any single image, together cutting errors by 38.2 % and 53.6 % respectively. Label transfer across diverse imagery further boosts data efficiency, yielding error reductions of 28.1 %–76.2 % and a 13‑fold decrease in performance variability across dates.
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video dat...
arXiv:2607. 06796v1 Announce Type: cross Abstract: Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing.
arXiv:2412. 01282v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks.
arXiv:2608.28161v1 Announce Type: cross Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are of...