HIFN-Transformer: Learnable Information-Theoretic Parameters for Interpretable Deep Classification

DeepU: Deeper Granular Within-Layer Machine Unlearning

Machine unlearning (MU) aims to remove the influence of selected data from trained models, offering an efficient alternative to full retraining. With the rise of increasingly stringent privacy regulations, including the right to be forgotten, machine learning models must incorporate mechanisms that ensure compliance while preserving performance. However, existing MU methods often suffer from performance degradation, and limited granularity, leaving residual traces that adversaries can exploit. We propose DeepU, a fine-grained unlearning framework that performs within-layer, weight-level analysis and quantifies the influence of individual weights using the signal-to-noise ratio (SNR) derived from the gradients of sensitive and non-sensitive data. DeepU categorizes weights as influential, intra-dependent, or non-influential and applies targeted weight updates: resetting or perturbing influential weights, decaying intra-dependent ones, and stabilizing the remaining ones via limited backpropagation. We evaluated DeepU on standard datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CelebA, demonstrating a 60–90% reduction in successful membership inference attacks with less than a 3% drop in accuracy, significantly outperforming existing benchmark methods. Re-tuning requires only 20.75 seconds and 102.47 MB, making DeepU up to 36.6 times faster than competing methods. We further evaluate robustness against Generative Adversarial Network (GAN) and diffusion-based model inversion attacks across white-box, black-box, and label-only threat models, demonstrating that DeepU consistently reduces reconstruction of forgotten data.

Hyperparameter Transfer in Graph Neural Networks

A transfer parameterization is developed and validated for GNNs trained with SGD, Adam, and AdamW that yields stable feature updates, learning rate transfer, and improved performance as width and depth increase, and provides a practical recipe for scaling GNNs across a variety of learning tasks and training scenarios.

An iHHO-Driven Hyperparameter Optimization Framework for Hybrid CNN–RNN Architectures in Fine-Grained Image Classification

Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations

Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining. Past research has demonstrated that quantization largely preserves classification accuracy; however, whether it also preserves the model's internal reasoning remains an open question. This study presents a systematic evaluation on how static PTQ affects the interpretability / explainability of five widely used CNN architectures: VGG19, ResNet18, EfficientNet-B0, DenseNet161, and MobileNetV2 at INT8 and INT4 precision. We employ a dual interpretability framework that combines Grad-CAM for spatial attention analysis with LIME for input-level feature attribution, and systematically compare full-precision and quantized models on two binary classification datasets. Interpretability is evaluated using three complementary metrics: the Pearson correlation coefficient, structural similarity index, and top-20% IoU to capture distributional and structural variations in model explanations, supplemented by deletion/insertion faithfulness analysis. The results show that classification accuracy is not a reliable indicator of interpretability stability under reduced precision. DenseNet161 maintains strong feature consistency across both precision levels, whereas EfficientNet-B0, despite achieving competitive spatial attention and classification accuracy at INT8 precision, exhibits a substantial degradation in input-level feature attribution. These findings have direct implications for the trustworthy deployment of quantized models in applications with high interpretability requirements, demonstrating that architecture selection is as important as the quantization strategy.

Architectural Inductive Bias in Knowledge Distillation: Disentangling Representational Similarity from Performance Transfer

Deploying deep learning models on resource-constrained edge devices necessitates a trade-off between computational efficiency and robustness. While Knowledge Distillation (KD) is widely used to compress large models into lightweight students, the impact of the Teacher’s architectural inductive bias on the Student’s safety profile remains underexplored. This paper investigates Cross-Architecture Distillation, specifically examining whether the robustness properties of a Vision Transformer (ViT) Teacher can be transferred to a Convolutional Neural Network (CNN) Student (MobileNetV2). Using Centered Kernel Alignment (CKA) and Fourier Spectral Analysis, we demonstrate that robustness transfer is driven by the learning of global shape biases— preserving the teacher's functional geometry—rather than strict feature mimicry. To isolate architectural bias from model capacity confounds and validate scalability to high-resolution data, our experiments are conducted on ImageNet-1k. Results reveal that a MobileNetV2 distilled from a ViT-B/16 Teacher achieves a significantly lower mean corruption error (mCE) compared to one distilled from a massive, capacity-matched ResNet-152 (p < 0.0001, Cohen's d = 21.50), despite comparable clean accuracy. Furthermore, we show that the ViT-distilled student effectively suppresses high-frequency noise, validating its resilience against adversarial perturbations. These findings establish a pareto-optimal strategy for deploying robust, lightweight models, proving that the choice of Teacher architecture is a critical hyperparameter for safety-critical edge applications.

Adaptive test-time augmentation via KL-regularized reinforcement learning for robust visual inference.

Deep neural networks often suffer significant accuracy degradation when exposed to real-world image corruptions and distribution shifts. To overcome the limitations of fixed, input-agnostic test-time augmentation (TTA), an adaptive framework is proposed that learns per-sample transformations via reinforcement learning. Augmentation selection is cast as a Markov decision process and proximal policy optimization (PPO) agents are trained to choose sample-specific transforms under a composite reward combining classifier confidence gains with a self-consistency KL-divergence penalty on the model's own softmax outputs, thereby preserving overall belief stability. On clean CIFAR-10 (1 000 samples), the adaptive ensemble raises accuracy from 88.5% (baseline) and 87.3% (static TTA) to 90.0% (+1.5 pp). On CIFAR-10-C (15 corruptions × 5 severities; 1 000 images per condition), pooled top-1 accuracy improves from 75.7% (baseline) and 74.3% (static TTA) to 76.4% (+0.7 pp), and exceeds a TENT entropy-minimization baseline (75.9%) while operating in a strictly label-free regime that updates no model weights. Per-corruption gains are consistently positive across noise, blur, weather, and compression distortions, with the adaptive policy outperforming TENT on texture and compression corruptions where input-space transforms are most effective. These findings demonstrate that learned, per-sample augmentation policies improve robustness and reliability of deep vision models under diverse image conditions, against a strong baseline classifier.