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Deep Learning using linear Support Vector Machines

The author substitutes a linear SVM for the softmax atop some architectures, then backpropagate the error of the primal problem to the whole network . This idea had already been proposed in the literature but with a standard hinge loss instead of the $L^2$-loss that the author uses.1 Because an $L^2$ loss penalizes mistakes more heavily than the standard hinge loss the author believes that:

the performance gain is largely due to the superior regularization effects of the SVM loss function, rather than an advantage from better parameter optimization.

Two natural questions pop up:

  1. Will using an SVM instead of a softmax help networks which already are heavily regularized? Note for instance that dropout2 seems not to have been used for the paper (but lots of Gaussian noise are added, which is a form of Tykhonov regularization3 and we know that dropout can act as an $L^2$ regularizer.4

  2. The softmax seems to be an important part of the reason why deep learning works so well is some situations.5 Will an SVM work in the same ones and why?

As to the implementation details, a one-vs rest multi-class SVM is directly substituted for the softmax layer:

For $K$ class problems, $K$ linear SVMs will be trained independently, where the data from the other classes form the negative cases.

Then the class with respect to which a given sample has maximal margin is taken to be the correct one. Note that this has the immediate disadvantage wrt. softmax, characteristic of SVMs, that the values obtained cannot be interpreted as probabilities anymore since the outputs $a_k (x) = w^{\top} x, k \in [K]$ of the SVM are not normalized. How does learning proceed? By backpropagating the error of the (unconstrained, primal) SVM’s objective:

$$ l (w ; \mathrm{x}, \mathrm{t}) = \underset{w}{\min} \frac{1}{2} | w |^2 + C \sum_{n = 1}^N \max (1 - w^{\top} x_n t_n, 0)^2, $$

where $(x_n, t_n) \in \mathbb{R}^d \times \lbrace - 1, 1 \rbrace $ are the outputs from the last layer and the training labels respectively and $w$ are the weights for the SVM. Note the square after the maximum: because the arguments of the max are linear, the whole function is differentiable with respect to each $x_n$. The gradient is:

$$ \nabla_x l (w ; x, t) = - 2 Ct_n w \max (1 - w^{\top} xt, 0) $$

Using this idea the author won the Facial Expression Recognition challenge at ICML 2013:

(…) using a simple Convolutional Neural Network with linear one-vs-all SVM at the top. Stochastic gradient descent with momentum is used for training and several models are averaged to slightly improve the generalization capabilities.

The details of the architecture are not clear, but the author reports

(…) using an 8 split/fold cross validation, with a image mirroring layer, similarity transformation layer, two convolutional filtering + pooling stages, followed by a fully connected layer with 3072 hidden penultimate hidden units. The hidden layers are all of the rectified linear type. other hyperparameters such as weight decay are selected using cross validation.

MNIST is another dataset where good results are obtained: first PCA down the data to 70 dimensions, then a shallow 512-512 network with an $L^2$-SVM atop (or softmax for comparison). Learning is done as usual with SGD with minibatch updates. More interestingly, a stronger regularization than that provided by the $L^2$-SVM alone was needed here:

To prevent overfitting and critical to achieving good results, a lot of Gaussian noise is added to the input.

With this setup, the network with $L^2$-SVM performed around 12% better than with softmax. It is however noteworthy that no other regularization techniques nor architectures were tested.

We now come to the more interesting question of why the method works. Is it a form of regularization or is the network easier to optimize? To test this

looked at the two final models’ loss under its own objective functions as well as the other objective. [Table 3]


Test error


Avg. cross entropy


Hinge loss squared



Table 3. Training objective including the weight costs.

Note how lower cross entropy actually had a higher error. Perhaps more interestingly the author

also initialized a ConvNet+Softmax model with the weights of the [ConvNet+SVM] that had 11.9% error. As further training is performed, the network’s error rate gradually increased towards 14%.

Which suggests that the $L^2$-SVM provides a better objective, probably through its regularization property.

  1. Some other prior work had been to train convnet (un)supervised, then use the output as input features for a SVM (but then training of the convnet is decoupled from the SVM’s objective function); train multiple stacked SVMs recursively (without joint fine-tuning).
  2. Improving neural networks by preventing co-adaptation of feature detectors .
  3. Training with noise is equivalent to Tikhonov regularization .
  4. Dropout training as adaptive regularization .
  5. Why does deep and cheap learning work so well? .