Fast Adaptation of Neural Networks

The ability to learn quickly from a few samples is a vital element of intelligence. Humans can reuse past knowledge and learn incredibly quickly. Also humans are able to interact with others to effectively guide their learning process. Computer vision systems for recognizing objects automatically from pixels are becoming commonplace in production systems. These modern computer vision systems use deep neural networks to automatically learn and recognize objects from data. Oftentimes, these deep neural networks used in production require a lot of data, take a long time to learn and forget old things when learning something new. We build upon previous methods called Prototypical Networks and Model-Agnostic Meta-Learning (MAML) that enables machines to learn to recognize new objects with very little supervision from the user. We extend these methods to the semi-supervised few-shot learning scenario, where the few labeled samples are accompanied with (potentially many) unlabeled samples. Our proposed methods are able to learn better by also making use of the additional unlabeled samples. We note that in many real-world applications the adaptation performance can be significantly improved by requesting the few labels through user feedback (active adaptation). Further, our proposed methods can also adapt to new tasks without any labeled examples (unsupervised adaptation) when the new task has the same output space as the training tasks do

AffinityNet: semi-supervised few-shot learning for disease type prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.Comment: To appear in CVPR 201