Title: Hybrid ACNs: Unifying Auto-Compressing and Residual Architectures
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: neural network architectures, compression, representation learning, residual connections

Abstract: We propose **Hybrid Auto-Compressing Networks (H-ACNs)**, unifying ACNs and ResNets under a single mathematical formulation controlled by trainable scalar residual weighting parameters per layer. Through theoretical analysis, we show that both architectures represent points on a continuous spectrum, with traditional ACNs and ResNets as special cases. Our key contribution is demonstrating that H-ACNs, when initialized close to ACNs, match ResNets training efficiency while preserving ACN-like robustness and compression capabilities. Experiments across vision transformers, MLP-mixers, and GPT-2 architectures show that H-ACNs achieve training convergence on par with ResNets,  while maintaining ACNs superior noise robustness and generalization. Furthermore, we discover that learned residual weights exhibit distinct connectivity patterns across tasks, namely, vision tasks favor local connectivity patterns resembling early visual cortex processing, while language tasks converge to modular hierarchical inter-layer structures similar to hierarchical language processing regions. We also examine how initialization impacts performance and connectivity, challenging the universality of the common ResNet-like initialization of residual weights. Overall, our results establish Hybrid ACNs as a practical framework for efficiently balancing training speed and representation quality, while revealing principles of how functional connectivity patterns should vary across domains, modalities, and tasks.