Source-linked AI summary
Deeply-Supervised Nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu
TL;DR
Deep networks face limited transparency and discriminativeness in hidden features, difficult training from exploding or vanishing gradients, and other optimization challenges. DSN adds companion objectives to hidden layers while retaining the output objective, and analyzes the formulation using stochastic-gradient techniques. The method reports performance gains on benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100, while relying on local convexity assumptions for its convergence analysis.
Problem
Deep-learning frameworks have limited hidden-layer transparency and discriminativeness, training difficulty from exploding or vanishing gradients, and incomplete mathematical understanding of their algorithmic behavior.
Method
DSN adds companion classification objectives to individual hidden layers alongside the overall output objective, with a stochastic-gradient analysis of the formulation.
Results
DSN reports performance gains over existing approaches across benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100.
Takeaways & Limitations
Direct hidden-layer supervision makes the learning process more transparent and provides feature-quality feedback within the network.
Takeaways & Limitations
The convergence discussion assumes the highly non-convex deep-learning objective is locally convex or strongly convex around an optimum.
Abstract
from arXiv · showhide
Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce "companion objective" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN).
1 Introduction
Deep learning offers rich hierarchical feature learning but faces concerns about hidden-layer transparency, training instability, and dependence on large datasets. DSN addresses these issues by adding direct supervision to hidden layers alongside the output objective.
- Motivation: Deep learning can automatically learn and fuse rich hierarchical features, especially with large training datasets.Its applications include image classification and speech recognition.
- Challenges: Current deep-learning frameworks face reduced hidden-layer transparency and discriminativeness, exploding or vanishing gradients, limited mathematical understanding, data dependence, and difficult manual tuning.These concerns motivate improvements to both feature learning and optimization.
- Proposed approach: DSN enforces direct and early supervision for hidden and output layers through companion objectives added to individual hidden layers.The companion objective acts as an additional constraint or regularization during learning.
- Proposed approach: The method is presented as a new formulation for supervised deep learning rather than layer-wise pre-training.The supplied related-work passage contrasts DSN with layer-wise supervised pre-training and emphasizes integrated backpropagation.
2 Deeply-Supervised Nets
Deeply-supervised nets add local classifiers and companion losses to hidden layers alongside the output objective, directly shaping intermediate features during integrated backpropagation. The formulation is intended to improve feature discriminativeness and training effectiveness while retaining the overall classification goal.
- Motivation: DSN uses hidden-layer classifier performance as feedback on feature quality, directly influencing weight updates toward discriminative feature maps.This supervision acts within the network rather than relying only on gradual propagation from the output layer.
- Formulation: DSN associates a companion local output and classifier with each hidden layer, allowing errors to backpropagate simultaneously from hidden and final outputs.The local output is analogous to the output of a truncated network.
- Formulation: The combined objective uses an output loss and hidden-layer companion losses, both described as squared hinge losses of prediction errors.The hidden-layer constraints directly encourage discriminative feature maps at individual layers.
- Optimization Analysis: The authors analyze DSN with stochastic-gradient techniques under local convexity assumptions, focusing on convergence rather than regularization effects on generalization.The analysis considers exploding and vanishing gradients and combines gradients from the output and companion objectives.
- Optimization Analysis: A feasible hidden-layer solution is shown to yield a feasible solution for the output objective, supporting the compatibility of local discriminativeness with the network-level goal.The construction extends a feasible solution through later layers using identity filters.
3 Experiments
Experiments on MNIST, CIFAR-10, CIFAR-100, and SVHN evaluate DSN under matched training protocols and compare it with competing CNN-based methods. DSN achieves lower reported classification errors while also producing more intuitive feature maps and faster, more robust training.
- Experimental setup: Experiments use SGD with mini-batches of 128, momentum 0.9, validation-selected learning rate and weight decay, and parameter counts matched to comparison architectures.
- MNIST: DSN-Softmax and DSN-SVM outperform competing CNN methods on MNIST, with DSN-SVM reaching 0.39% classification error using a single model without whitening or augmentation.At 500 training samples, DSN-SVM provides a 26% gain over CNN-Softmax.
- MNIST: 26% gain of DSN-SVM over CNN-Softmax is reported at 500 MNIST training samples.
- CIFAR-10 and CIFAR-100: 9.78% error without augmentation and 8.22% with augmentation are reported for DSN on CIFAR-10.The augmented result is described as the best known result to the authors.
- Training and learned features: DSN guides early layers with direct classification loss, leading to faster convergence, reduced hyperparameter-tuning burden, and more intuitive feature maps than CNN.The first convolutional layer shows 4.55 times greater gradient variance for DSN than CNN.
- CIFAR-10 and CIFAR-100: 34.57% CIFAR-100 classification error is reported for DSN, while the method comparison includes Stochastic Pooling at 42.51% and Network in Network at 35.68%.The authors state that the performance boost is consistent across CIFAR-10 and CIFAR-100.
4 Conclusions
DSN presents a new formulation intended to make deep-learning processes more transparent, with reported performance enhancement over existing approaches and a stochastic-gradient perspective on the formulation.
- DSN is introduced as a new formulation for making the learning process in deep learning more transparent.
- The formulation reports evident performance enhancement over existing approaches.
- A stochastic gradient view provides additional insight into understanding the DSN formulation.