Shenzhen Technology University

This work turns the sparsity coefficient of convolutional sparse coding from a manually fixed hyperparameter into a differentiable variable jointly learned with network parameters through unfolded FISTA iterations, interprets that coefficient within an information bottleneck framework as the controller of the compression-retention trade-off, achieves competitive clean-data recognition on CIFAR-10/100 and ImageNet-1K with greatly improved robustness under various input perturbations, and introduces a label-free post-training adaptation strategy that adjusts compression strength using a small set of unlabeled corrupted samples while keeping the main network parameters frozen.

This work turns the sparsity coefficient of convolutional sparse coding from a manually fixed hyperparameter into a differentiable variable jointly learned with network parameters through unfolded FISTA iterations, interprets that coefficient within an information bottleneck framework as the controller of the compression-retention trade-off, achieves competitive clean-data recognition on CIFAR-10/100 and ImageNet-1K with greatly improved robustness under various input perturbations, and introduces a label-free post-training adaptation strategy that adjusts compression strength using a small set of unlabeled corrupted samples while keeping the main network parameters frozen.

The work proposes HEAL, which applies causal noise intervention to multi-head outputs and counterfactual Difference-in-Differences to categorize attention heads into redundant, visual, language, and synergy types, finds that hallucinations occur when the visual-language information distribution inside synergy heads drifts away from a healthy equilibrium rather than correlating strongly with the quantity or strength of modality-specific heads, and accordingly injects dynamic calibration factors into the value vectors of synergy heads at inference time, reducing hallucinations across multiple MLLMs and benchmarks.

The work proposes HEAL, which applies causal noise intervention to multi-head outputs and counterfactual Difference-in-Differences to categorize attention heads into redundant, visual, language, and synergy types, finds that hallucinations occur when the visual-language information distribution inside synergy heads drifts away from a healthy equilibrium rather than correlating strongly with the quantity or strength of modality-specific heads, and accordingly injects dynamic calibration factors into the value vectors of synergy heads at inference time, reducing hallucinations across multiple MLLMs and benchmarks.