This paper is about regularizing deep convolutional networks (ConvNets) based on an adaptive multi-objective framework for transfer learning with limited training data in the target domain. Recent advances of ConvNets regularization in this context are commonly due to the use of additional regularization objectives. They guide the training away from the target task using some concrete tasks. Unlike those related approaches, we report that an objective without a concrete goal can serve surprisingly well as a regularizer. In particular, we demonstrate Pseudo-task Regularization (PtR) which dynamically regularizes a network by simply attempting to regress image representations to a pseudo-target during fine-tuning. Through numerous experiments, the improvements on classification accuracy by PtR are shown greater or on a par to the recent state-of-the-art methods. These results also indicate a room for rethinking on the requirements for a regularizer, i.e., if specifically designed task for regularization is indeed a key ingredient. The contributions of this paper are: a) PtR provides an effective and efficient alternative for regularization without dependence on concrete tasks or extra data; b) desired strength of regularization effect in PtR is dynamically adjusted and maintained based on the gradient norms of the target objective and the pseudo-task.
Regularization methods that are commonly used for fine-tuning ConvNets can be generally grouped into four categories: data perturbation, parameter norm penalty, dropout, and multi-task learning. Image augmentation as a popular form of image perturbation has been proven to be particularly useful to prevent ConvNets from overfitting. In this paper, we also assume to perturb our training instances by random augmentations. The supervision signal can be perturbed for better regularization as well. This can be achieved by learning to predict soft targets rather than hard binary ones as in @cite_11 . In this work, label perturbation is not considered so that we can deliver more ablated studies on the effectiveness of auxiliary training objectives.
@cite_11
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Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22 accuracy promotion and on CIFAR-100 9.63 promotion).