A Meta-Learning Approach for Custom Model Training

Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. when there are only a few classes and training examples available in the target task), meta-learning approaches that optimize for future task learning have outperformed the typical transfer approach of initializing model weights from a pre-trained starting point. But as we experimentally show, meta-learning algorithms that work well in the few-class setting do not generalize well in many-shot and many-class cases. In this paper, we propose a joint training approach that combines both transfer-learning and meta-learning. Benefiting from the advantages of each, our method obtains improved generalization performance on unseen target tasks in both few- and many-class and few- and many-shot scenarios.Comment: AAAI 201

Analyzing satellite images by apply deep learning instance segmentation of agricultural fields

This novel research focuses on multi-exposure satellite images of agricultural fields using image analysis and deep learning techniques. The development of image edge smoothening system using CNN is in hot pursuit, with special attention being given to the smoothening of all the edges of image. Given its high propensity to meta-size, going hand in hand with severe decreases in preservation rates, and the high inter-edge variability in image appearance, as well as a strong requirement on the training of the physician properly de-noising an image can be considered a daunting task. The purpose of this advance research is to use a deep learning and image analysis pipeline for multi-exposure satellite image for the segmentation of edges in an image using with hybrid techniques in deep learning and imaging. The literature review of different papers was conducted with different imaging model architectures. The CNN custom model was created for the task, and deep learning technique (CNN) was used with different levels of fine tuning of hybrid satellite image analysis techniques. Screening for high edge filter to identify edges at high accuracy has been under debate. The custom deep learning model architectures were designed to represent different depths. Additionally, deep learning CNN model was created to represent traditional automated image analysis approach. The study also attempts to find solutions to practical deep learning challenges such as low training speed and lack of transparency with an accuracy of 98.17% absolutely

An investigation of a deep learning based malware detection system

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and a False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.Comment: 13 Pages, 4 figure

Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.Comment: Update baseline