揭开深度网络的幕布
Pulling Back the Curtain on Deep Networks
AI总结：
本文提出通过输入条件的仿射算子理解深度网络，利用逆向软化和迭代增强重构目标神经元编码的局部结构，提供统一视角整合SmoothGrad等方法，生成语义对齐的解释。
摘要：
在线性模型中，可视化权重向量自然揭示模型偏好输入方向，但通过梯度或梯度上升扩展到深度网络时往往产生脆弱或对抗性特征。我们主张深度网络应视为输入条件的仿射算子，其自然对偶作用将神经元偏好方向拉回输入空间。进一步通过反向软化和迭代增强重构目标神经元编码的局部结构。这为SmoothGrad、B-cos式对齐和特征强调等不同思想提供了统一视角。生成的语义拉回（SP）产生视觉对齐、类条件的后处理解释，强调语义特征，促进连贯的反事实扰动，并保持理论基础。在卷积架构（ResNet50、VGG）和基于Transformer的模型（PVT）中，语义拉回在忠实性、稳定性及目标敏感性基准上取得最佳整体平衡，同时保持通用、计算高效且易于集成到现有深度学习流程中。
英文摘要：
In linear models, visualizing a weight vector naturally reveals the model's preferred input direction, but extending this intuition to deep networks via gradients or gradient ascent often yields brittle or adversarial-looking features. We argue that deep networks are better understood as input-conditioned affine operators, whose natural adjoint action pulls a neuron's preferred direction back to input space. We further refine this representation by backward-only softening and iterative enhancement to reconstruct coherent local structures encoded by the target neuron. This provides a unifying perspective on previously disparate ideas such as SmoothGrad, B-cos-style alignment, and Feature Accentuation. The resulting Semantic Pullbacks (SP) generate perceptually aligned, class-conditional post-hoc explanations that emphasize semantically meaningful features, facilitate coherent counterfactual perturbations, and remain theoretically grounded. Across convolutional architectures (ResNet50, VGG) and transformer-based models (PVT), Semantic Pullbacks achieve the best overall trade-off across faithfulness, stability, and target-sensitivity benchmarks, while remaining general, computationally efficient, and readily integrable into existing deep learning pipelines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
