Face Model Compression by Distilling Knowledge from Neurons

The recent advanced face recognition systems were built on large Deep Neural Networks (DNNs) or their ensembles, which have millions of parameters. However, the expensive computation of DNNs make their deployment difficult on mobile and embedded devices. This work addresses model compression for face recognition, where the learned knowledge of a large teacher network or its ensemble is utilized as supervision to train a compact student network. Unlike previous works that represent the knowledge by the soften label probabilities, which are difficult to fit, we represent the knowledge by using the neurons at the higher hidden layer, which preserve as much information as the label probabilities, but are more compact. By leveraging the essential characteristics (domain knowledge) of the learned face representation, a neuron selection method is proposed to choose neurons that are most relevant to face recognition. Using the selected neurons as supervision to mimic the single networks of DeepID2+ and DeepID3, which are the state-of-the-art face recognition systems, a compact student with simple network structure achieves better verification accuracy on LFW than its teachers, respectively. When using an ensemble of DeepID2+ as teacher, a mimicked student is able to outperform it and achieves 51.6× compression ratio and 90× speed-up in inference, making this cumbersome model applicable on portable devices. Introduction As the emergence of big training data, Deep Neural Networks (DNNs) recently attained great breakthroughs in face recognition [23, 20, 21, 22, 19, 15, 29, 30, 28] and become applicable in many commercial platforms such as social networks, e-commerce, and search engines. To absorb massive supervision from big training data, existing works typically trained a large DNN or a DNN ensemble, where each DNN consists of millions of parameters. Nevertheless, as face recognition shifts toward mobile and embedded devices, large DNNs are computationally expensive, which prevents them from being deployed to these devices. It motivates research of using a small network to fit very large training ∗indicates co-first authors who contributed equally. Copyright c © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. data. This work addresses model compression of DNNs for face recognition, by incorporating domain knowledge of learning face representation. There have been several attempts [1, 7, 18] in literature to compress DNNs, so as to make their deployments easier, where a single network (i.e. a student) was trained by using the knowledge learned with a large DNN or a DNN ensemble (i.e. a teacher) as supervision. This knowledge can be simply represented as the probabilities of label predictions by employing the softmax function [10]. Compared with the original 1-of-K hard labels, the label probabilities encode richer relative similarities among training samples and can train a DNN more effectively. However, this representation loses much information because most of the probabilities are close to zeros after squashed by softmax. To overcome this problem, Ba and Caruana [1] represented the learned knowledge by using the logits, which are the values before softmax activation but zero-meaned, revealing the relationship between labels as well as the similarities among samples in the logit space. However, as these unconstrained values (e.g. the large negatives) may contain noisy information that overfits the training data, using them as supervision limits the generalization ability of the student. Recently, Hinton et al. [7] showed that both the label probabilities and zero-meaned logits are two extreme outputs of the softmax functions, where the temperature becomes one and positive infinity, respectively. To remove target noise, they empirically searched for a suitable temperature in the softmax function, until it produced soften probabilities that were able to disclose the similarity structure of data. As these soften target labels comprise much valuable information, a single student trained on them is able to mimic the performance of a cumbersome network ensemble. Despite the successes of [7], our empirical results show that training on soft targets is difficult to converge when compressing DNNs for face recognition. Previous studies [23, 24, 20, 19] have shown that the face representation learned from classifying larger amount of identities in the training data (e.g. 250 thousand in [24]) may have better generalization capacity. In face recognition, it seems difficult to fit soft targets with high dimensionality, which makes convergence slow. In this work, we show that instead of using soft targets in the output layer, the knowledge of the teacher can also be obtained from the neurons in the top hidden layer, which preserve as much information as the soft targets (as the soft targets are predicted from these neurons) but are more compact, e.g. 512 versus 12,994 according to the net structure in [21]. As these neurons may contain noise or information not relevant to face recognition, they are further selected according to the usefulness of knowledge captured by them. In particular, the selection is motivated by three original observations (domain knowledge) of face representation disclosed in this work, which are naturally generalized to all DNNs trained by distinguishing massive identities, such as [19, 23, 24, 22]. (1) Deeply learned face representation by the face recognition task is a distributed representation [6] over face attributes, including the identity-related attributes (IA), such as gender, race, and shapes of facial components, as well as the identity non-related attributes (NA), such as expression, lighting, and photo quality. This observation implies that each attribute concept is explained by having some neurons being activated while each neuron is involved in representing more than one attribute, although attribute labels are not provided during training. (2) However, a certain amount of neurons are selective to NA or both NA and IA, implying that the distributed representation is neither invariant nor completely factorized, because attributes in NA are variations that should be removed in face recognition, whereas these two factors (NA and IA) are presented and coupled in some neurons. (3) Furthermore, a small amount of neurons are inhibitive to all attributes and server as noise. With these observations, we cast neuron selection as inference on a fully-connected graph, where each node represents attribute-selectiveness of neuron and each edge represents correlation between neurons. An efficient mean field algorithm [9] enables us to select neurons that are more selective or discriminative to IA, but less correlated with each other. As a result, the features of the selected neurons are able to maintain the inter-personal discriminativeness (i.e. distributed and factorized to explain IA), while reducing intra-personal variations (i.e. invariant to NA). We employ the features after neuron selection as regression targets to train the student. To evaluate neuron selection, we employ DeepID2+ [21] as a teacher (T1), which achieved state-of-the-art performance on LFW benchmark [8]. This work is chosen as an example because it successfully incorporated multiple complex components for face recognition, such as local convolution [12], ranking loss function [19], deeply supervised learning [13], and model ensemble [17]. The effectiveness of all these components in face recognition have been validated by many existing works [19, 23, 24, 27]. Evaluating neuron selection on it demonstrates its capacity and generalization ability on mimicking functions induced by different learning strategies in face recognition. With neuron selection, a student with simple network structure is able to outperform a single network of T1 or its ensemble. Interestingly, this simple student generalizes well to mimic a deeper teacher (T2), DeepID3 [22], which is a recent extension of DeepID2+. Although there are other advanced methods [24, 19] in face recognition, [21, 22] are more suitable to be taken as baselines. They outperformed [24] and achieved comparable result with [19] on LFW with much smaller size of training data and identities, i.e. 290K images [21] compares to 7.5M images [24] and 200M images [19]. We cannot compare with [24, 19] because their data are unavailable. Three main contributions of this work are summarized as below. (1) We demonstrate that more compact supervision converge more efficiently, when compressing DNNs for face recognition. Soft targets are difficult to fit because of high dimensionality. Instead, neurons in the top hidden layers are proper supervision, as they capture as much information as soft targets but more compact. (2) Three valuable observations are disclosed from the deeply learned face representation, identifying the usefulness of knowledge captured in these neurons. These observations are naturally generalized to all DNNs trained on face images. (3) With these observations, an efficient neuron selection method is proposed for model compression and its effectiveness is validated on T1 and T2. Face Model Compression Training Student via Neuron Selection The merit behind our method is to select informative neurons in the top hidden layer of a teacher, and adopt the features (responses) of the chosen neurons as supervision to train a student, mimicking the teacher’s feature space. We formulate the objective function of model compression as a regression problem given a training set D = {Ii, fi}i=1,