arXiv AI
Jul 16
Barnamala: Parameter-Efficient Handwritten Devanagari Recognition at Benchmark Saturation
arXiv:2607. 13689v1 Announce Type: cross Abstract: We built a compact convolutional network (1.
Barnamala: Parameter-Efficient Handwritten Devanagari Recognition at Benchmark Saturation
We built a compact convolutional network (1. 11 M parameters) for 46-class DHCD Devanagari recognition and reached 99.
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arXiv Machine Learning
Jun 2
A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation
arXiv:2606. 01973v1 Announce Type: new Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes.
arXiv AI
Jul 23
Decodable but Not Detectable: A Leakage Fingerprint for Near-OOD Benchmarks
arXiv:2607. 19393v1 Announce Type: cross Abstract: While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.
arXiv Machine Learning
Jul 14
Student Capacity Moderates Knowledge Distillation Effectiveness: A Systematic Study Across ResNet Teacher-Student Pairs on CIFAR-10
arXiv:2605. 31191v2 Announce Type: replace Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10.
arXiv Machine Learning
Aug 12
SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features
arXiv:2608. 10709v1 Announce Type: new Abstract: Quantization-Aware Training (QAT) enables the deployment of quantized models with minimal accuracy degradation.
arXiv AI
Aug 6
Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection
arXiv:2604. 17388v3 Announce Type: replace-cross Abstract: Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables.
arXiv Machine Learning
Jul 15
Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
arXiv:2512. 23043v2 Announce Type: replace Abstract: Federated Averaging (FedAvg) often degrades under non-IID client data, but it remains unclear whether this degradation reflects the loss of client-learned representations or a failure to use representations that are still present.
arXiv Machine Learning
Jun 2
Single-Channel Tissue Segmentation via Cross-Modal Distillation from Foundation Models
arXiv:2606. 00928v1 Announce Type: cross Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin), that together encode richer spatial context than single-channel imaging alone.
arXiv AI
Jul 1
Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition
arXiv:2606. 31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.
arXiv AI
Jul 8
Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
arXiv:2607. 05891v1 Announce Type: cross Abstract: Coreset selection aims to identify a small and highly representative subset of a massive dataset for efficient model training.
arXiv AI
Jun 24
Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring
arXiv:2606. 24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged.
arXiv Machine Learning
Aug 4
Subtype Robustness Is Not Just Accuracy: Calibration Under Unseen Subtype Shift
arXiv:2608. 00928v1 Announce Type: new Abstract: Subtype robustness asks whether a model keeps the correct coarse prediction when test examples come from fine-grained subtypes absent from training but still inside a known coarse category.