Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning

Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g., an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by unseen class data synthesis and robust projection function learning. Specifically, a novel semantic data synthesis strategy is proposed, by which semantic class prototypes (e.g., attribute vectors) are used to simply perturb seen class data for generating unseen class ones. As in any data synthesis/hallucination approach, there are ambiguities and uncertainties on how well the synthesised data can capture the targeted unseen class data distribution. To cope with this, the second contribution of this work is a novel projection learning model termed competitive bidirectional projection learning (BPL) designed to best utilise the ambiguous synthesised data. As a third contribution, we show that the proposed ZSL model can be easily extended to few-shot learning (FSL) by again exploiting semantic (class prototype guided) feature synthesis and competitive BPL. Extensive experiments show that our model achieves the state-of-the-art results on both problems

Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. In this work, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we define a recurrent neural network over superclasses and then plug it into a convolutional neural network for enforcing the superclass hierarchy. Second, to further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task—few-shot learning (FSL). Extensive experiments show that the proposed model outperforms the state-of-the-art alternatives in both ZSL and FSL tasks

Biomechanical characteristics of a novel interspinous distraction fusion device in the treatment of lumbar degenerative diseases: a finite element analysis

Abstract Background A novel interspinous distraction fusion (ISDF) device has been used to treat lumbar degenerative diseases. As a minimally invasive technique, ISDF differs from the traditional interspinous process distraction devices. Currently, biomechanical studies on ISDF are rare. Objective To investigate the biomechanical properties of the ISDF device (BacFuse) which is used to treat lumbar degenerative diseases. Methods Three-dimensional L3-L5 models were created. The models were divided into four groups: intact (M1), local decompression alone (M2), internal fixation alone (M3) and local decompression combined with internal fixation (M4), based on different surgical procedures. Local laminectomy was performed to resect the lower part of the L4 lamina and the upper part of the L5 lamina at the right lamina of L4/5 in the M2 and M4 groups. After meshing the models elements, Abaqus were used to perform the finite element (FE) analysis. The intervertebral range of motion (ROM) was measured during flexion, extension, left lateral bending, right lateral bending, left rotation and right rotation under a follower load of 400 N with a 7.5Nm moment. The distributions of disc and facet joint stresses were observed and recorded. Spinal vertebral stress was compared, and internal fixation device stress was observed. Results The ROM of L4/5 in M2 increased in flexion, extension, left lateral bending, right lateral bending, left rotation and right rotation compared with that in M1. In all motion directions, the ROM at L4/5 decreased, and the ROM at L3/4 increased after implantation of the ISDF device in M3 and M4 groups. The disc stress and facet joint stresses in the instrumented segment decreased after implantation of the ISDF device. The spinous process loaded a certain amount of stress in M3 and M4 groups. The spikes of the internal fixation device were loaded with the maximum stress. Conclusion BacFuse exhibited a reduction in intervertebral ROM, as well as decreased stress on the intervertebral disc and facet joint, while also demonstrating a discernible impact on the upper adjacent segment