The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
Snapshot Ensembles: Train 1, get M for free
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively.
verdicts
UNVERDICTED 4representative citing papers
Representations learned by large AI models are converging toward a shared statistical model of reality.
CLIP-guided selection of external data plus staged NAFNet training and inference fusion provides an effective pipeline for nighttime image dehazing in the NTIRE 2026 challenge.
CNNs applied to the NIH malaria dataset reach 97.77% accuracy for parasite detection in segmented RBC patches using 5-fold cross-validation and holdout testing.
citing papers explorer
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Random Vector Functional Link Neural Network based Ensemble Deep Learning
The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
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The Platonic Representation Hypothesis
Representations learned by large AI models are converging toward a shared statistical model of reality.
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CLIP-Guided Data Augmentation for Night-Time Image Dehazing
CLIP-guided selection of external data plus staged NAFNet training and inference fusion provides an effective pipeline for nighttime image dehazing in the NTIRE 2026 challenge.
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Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks
CNNs applied to the NIH malaria dataset reach 97.77% accuracy for parasite detection in segmented RBC patches using 5-fold cross-validation and holdout testing.