We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation.
In @cite_1 , a convolutional auto-encoder was used for disentanglement and generating normal and albedo images. However recent advances in skip-connection based convolutional encoder-decoder architectures for image to image translations @cite_36 @cite_33 @cite_12 have also motivated their use in @cite_14 . Even though skip connection based architectures are successful in transferring high frequency informations from input to output, they fail to produce meaningful disentanglement of both low and high frequencies. Our proposed decomposition architecture uses residual block based connections that allow the flow of high frequency information from input to output while each layer learns both high and low frequency features. A residual block based architecture was used for image to image translation in @cite_20 for style transfer and in a completely different domain to learn a latent subspace with Generative Adversarial Networks @cite_37 .
@cite_37 @cite_14 @cite_33 @cite_36 @cite_1 @cite_20 @cite_12
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Deep architectures using identity skip-connections have demonstrated groundbreaking performance in the field of image classification. Recently, empirical studies suggested that identity skip-connections enable ensemble-like behaviour of shallow networks, and that depth is not a solo ingredient for their success. Therefore, we examine the potential of identity skip-connections for the task of Speech Emotion Recognition (SER) where moderately deep temporal architectures are often employed. To this end, we propose a novel architecture which regulates unimpeded feature flows and captures long-term dependencies via gate-based skip-connections and a memory mechanism. Our proposed architecture is compared to other state-of-the-art methods of SER and is evaluated on large aggregated corpora recorded in different contexts. Our proposed architecture outperforms the state-of-the-art methods by 9 - 15 and achieves an Unweighted Accuracy of 80.5 in an imbalanced class distribution. In addition, we examine a variant adopting simplified skip-connections of Residual Networks (ResNet) and show that gate-based skip-connections are more effective than simplified skip-connections. We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression. We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http: mi.eng.cam.ac.uk projects segnet . It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images from their hidden representations, in most commonly used network architectures. In this paper we show that this loss of information is not a necessary condition to learn representations that generalize well on complicated problems, such as ImageNet. Via a cascade of homeomorphic layers, we build the i-RevNet, a network that can be fully inverted up to the final projection onto the classes, i.e. no information is discarded. Building an invertible architecture is difficult, for example, because the local inversion is ill-conditioned, we overcome this by providing an explicit inverse. An analysis of i-RevNet’s learned representations suggests an explanation of the good accuracy by a progressive contraction and linear separation with depth. To shed light on the nature of the model learned by the i-RevNet we reconstruct linear interpolations between natural images representations.