The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
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Efficientnet: Rethinking model scaling for convolutional neural networks
17 Pith papers cite this work. Polarity classification is still indexing.
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SMCNet applies a complex-valued CNN to mmWave radar IQ data for high-accuracy surface material classification across multiple and unseen sensing distances.
The DFDC dataset is the largest public collection of face-swapped videos and supports detectors that generalize to in-the-wild deepfakes.
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
Inpainting auxiliary task improves clustering of embeddings for individual zebrafish identification based on skin patterns.
DBLP is a training-phase-aware bounded-loss transport protocol that reduces end-to-end distributed ML training time by 24.4% on average (up to 33.9%) and achieves up to 5.88x communication speedup during microbursts while maintaining comparable test accuracy.
Equinox uses a barrier-function-derived marginal cost to enable value-based adaptive scheduling and neighbor offloading in energy-constrained satellite constellations, yielding 20-31% throughput gains and higher battery reserves in simulation.
Models predicting human authenticity judgments produce inconsistent attribution maps across architectures, showing that explanations are non-identifiable.
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
DYMAPIA builds dynamic anomaly masks from Fourier spectra, texture, edges, and optical flow to guide a lightweight DistXCNet classifier, reporting over 99% accuracy and F1 on FF++, Celeb-DF, and VDFD.
RDCNet reports state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof by combining random dilated convolutions with multi-branch and attention modules.
Cello Evaluator is a real-time postural feedback system for cellists running on current Android phones via on-device computer vision, validated as user-friendly by experts.
The NTIRE 2026 challenge finds that large foundation models combined with ensembles and degradation-aware training produce the most robust deepfake detectors.