In this study, we propose an incremental learning approach based on a machine-machine
interaction via relative attribute feedbacks that exploit comparative relationships among top …

Recently, people pay more attention to catastrophic forgetting problem, that is, the ability of
the model to recognize old tasks decreases dramatically when new tasks are added …

Active learning provides useful tools to reduce annotation costs without compromising
classifier performance. However it traditionally views the supervisor simply as a labeling …

We propose a novel approach for estimating the difficulty and transferability of supervised
classification tasks. Unlike previous work, our approach is solution agnostic and does not …

An important challenge for neural networks is to learn incrementally, ie, learn new classes
without catastrophic forgetting. To overcome this problem, generative replay technique has …

Deep predictive models rely on human supervision in the form of labeled training data.
Obtaining large amounts of annotated training data can be expensive and time consuming …

Catastrophic forgetting in neural networks during incremental learning remains a
challenging problem. Previous research investigated catastrophic forgetting in fully …

We introduce an approach for incremental learning that preserves feature descriptors of
training images from previously learned classes, instead of the images themselves, unlike …

It is useful to automatically compare images based on their visual properties---to predict
which image is brighter, more feminine, more blurry, etc. However, comparative models are …

An important problem in artificial intelligence is to develop an efficient system that can adapt
to new knowledge in an incremental manner without forgetting previously learned …