GIFSL - grafting based improved few-shot learning

A few-shot learning model generally consists of a feature extraction network and a classification module. In this paper, we propose an approach to improve few-shot image classification performance by increasing the representational capacity of the feature extraction network and improving the quality of the features extracted by it. The ability of the feature extraction network to extract highly discriminative features from images is essential to few-shot learning. Such features are generally class agnostic and contain information about the general content of the image. Our approach improves the training of the feature extraction network in order to enable them to produce such features. We train the network using filter-grafting along with an auxiliary self-supervision task and a knowledge distillation procedure. Particularly, filter-grafting rejuvenates unimportant (invalid) filters in the feature extraction network to make them useful and thereby, increases the number of important filters that can be further improved by using self-supervision and knowledge distillation techniques. This combined approach helps in significantly improving the few-shot learning performance of the model. We perform experiments on several few-shot learning benchmark datasets such as mini-ImageNet, tiered-ImageNet, CIFAR-FS, and FC100 using our approach. We also present various ablation studies to validate the proposed approach. We empirically show that our approach performs better than other state-of-the-art few-shot learning methods.</p

T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events

Spiking silicon cochlea sensors encode sound as an asynchronous stream of spikes from different frequency channels. The lack of labeled training datasets for spiking cochleas makes it difficult to train deep neural networks on the outputs of these sensors. This work proposes a self-supervised method called Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network pretrained on spectrogram features so that the network works with the cochlea event features. T-NGA training requires only temporally aligned audio spectrograms and event features. Our experiments show that the accuracy of the grafted network was similar to the accuracy of a supervised network trained from scratch on a speech recognition task using events from a software spiking cochlea model. Despite the circuit non-idealities of the spiking silicon cochlea, the grafted network accuracy on the silicon cochlea spike recordings was only about 5% lower than the supervised network accuracy using the N-TIDIGITS18 dataset. T-NGA can train networks to process spiking audio sensor events in the absence of large labeled spike datasets.Comment: 5 pages, 4 figures; accepted at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 202