Convolutional Networks with Adaptive Inference Graphs (1711.11503v3)

Abstract: Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which layer to compute next. In this work, we propose convolutional networks with adaptive inference graphs (ConvNet-AIG) that adaptively define their network topology conditioned on the input image. Following a high-level structure similar to residual networks (ResNets), ConvNet-AIG decides for each input image on the fly which layers are needed. In experiments on ImageNet we show that ConvNet-AIG learns distinct inference graphs for different categories. Both ConvNet-AIG with 50 and 101 layers outperform their ResNet counterpart, while using 20% and 38% less computations respectively. By grouping parameters into layers for related classes and only executing relevant layers, ConvNet-AIG improves both efficiency and overall classification quality. Lastly, we also study the effect of adaptive inference graphs on the susceptibility towards adversarial examples. We observe that ConvNet-AIG shows a higher robustness than ResNets, complementing other known defense mechanisms.

• The paper proposes ConvNet-AIG, which adaptively constructs its inference graph by selecting relevant layers based on input images.
• It achieves significant efficiency gains, reducing computations by up to 38% compared to standard ResNet models while improving classification accuracy.
• The approach employs a differentiable gating mechanism, enhancing robustness against adversarial attacks and making it ideal for resource-constrained applications.

Convolutional Networks with Adaptive Inference Graphs

The paper "Convolutional Networks with Adaptive Inference Graphs" by Andreas Veit and Serge Belongie introduces a novel approach to convolutional neural networks (ConvNets) that challenges the conventional fixed feed-forward architectures. The authors propose ConvNet-AIG, a network that dynamically constructs its computational graph based on the input image, thereby optimizing computational efficiency and improving classification accuracy. This work introduces an adaptive mechanism that allows the network to decide which subset of layers to execute, conditioned on the input, thus deviating from the static nature of traditional convolutional networks.

Key Contributions

1. Adaptive Graph Construction: The primary contribution is the design of convolutional networks that adaptively define their inference graphs. Unlike standard ConvNets, which execute all layers regardless of the input, ConvNet-AIG learns to select layers relevant to the specific input image, akin to a decision tree. This approach enables the network to navigate through layers more intelligently, bypassing redundant computations.
2. Efficiency Gains: The paper demonstrates significant reductions in computational requirements while maintaining or improving classification accuracy. Specifically, ConvNet-AIG achieves approximately 20% and 38% reductions in computations compared to ResNet models with 50 and 101 layers, respectively, on ImageNet, while also outperforming them in accuracy.
3. Robustness to Adversarial Attacks: An interesting observation is ConvNet-AIG's enhanced robustness against adversarial examples. This robustness persists even when incorporating additional defense mechanisms, suggesting that the adaptive nature of the network's inference graph contributes to its resilience against adversarial manipulations.
4. Differentiable Gating Mechanism: The technical implementation includes a gating mechanism that makes discrete decisions about layer execution, facilitated by differentiable approximations like the Gumbel-Softmax. This allows the gates, modeled as discrete random variables, to joint train with convolutional weights effectively.

Implications and Future Directions

This research opens up multiple avenues in both theoretical and practical aspects of deep learning. Theoretically, it highlights the potential for dynamic architectures that adjust their topology in response to the data. Such flexibility can lead to more generalizable models that are less prone to overfitting, as they avoid committing computational resources to unrelated tasks.

Practically, adaptive models like ConvNet-AIG can drive efficiency in resource-constrained environments by significantly reducing unnecessary computations, making them attractive for deployment in applications like mobile and edge computing. The concept may also be extended to different neural architectures, including densely connected or attention-based networks, to explore further improvements in efficiency and adaptability.

Moreover, the observed robustness of ConvNet-AIG introduces possibilities for developing more secure AI systems resistant to adversarial attacks. Future research might focus on refining the gating mechanisms or exploring alternative stochastic processes as a means of enhancing network security.

In conclusion, the ConvNet-AIG framework represents an advancement towards more intelligent and efficient neural network designs. By challenging the fixed structure paradigm, it sets a precedent for future models that prioritize adaptable inference mechanisms aligned with the specifics of input data, thus achieving enhanced efficiency and robustness.