5,061 research outputs found
Context Embedding Networks
Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Existing models for learning embeddings from the crowd typically
make simplifying assumptions such as all individuals estimate similarity using
the same criteria, the list of criteria is known in advance, or that the crowd
workers are not influenced by the data that they see. To overcome these
limitations we introduce Context Embedding Networks (CENs). In addition to
learning interpretable embeddings from images, CENs also model worker biases
for different attributes along with the visual context i.e. the visual
attributes highlighted by a set of images. Experiments on two noisy crowd
annotated datasets show that modeling both worker bias and visual context
results in more interpretable embeddings compared to existing approaches.Comment: CVPR 2018 spotligh
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Multi-label image classification is a fundamental but challenging task
towards general visual understanding. Existing methods found the region-level
cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations
(i.e., object labels and bounding boxes) for effective learning of the
object-level visual features. In this paper, we propose a novel and efficient
deep framework to boost multi-label classification by distilling knowledge from
weakly-supervised detection task without bounding box annotations.
Specifically, given the image-level annotations, (1) we first develop a
weakly-supervised detection (WSD) model, and then (2) construct an end-to-end
multi-label image classification framework augmented by a knowledge
distillation module that guides the classification model by the WSD model
according to the class-level predictions for the whole image and the
object-level visual features for object RoIs. The WSD model is the teacher
model and the classification model is the student model. After this cross-task
knowledge distillation, the performance of the classification model is
significantly improved and the efficiency is maintained since the WSD model can
be safely discarded in the test phase. Extensive experiments on two large-scale
datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior
performances over the state-of-the-art methods on both performance and
efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table
Deep learning from crowds
Over the last few years, deep learning has revolutionized the field of
machine learning by dramatically improving the state-of-the-art in various
domains. However, as the size of supervised artificial neural networks grows,
typically so does the need for larger labeled datasets. Recently, crowdsourcing
has established itself as an efficient and cost-effective solution for labeling
large sets of data in a scalable manner, but it often requires aggregating
labels from multiple noisy contributors with different levels of expertise. In
this paper, we address the problem of learning deep neural networks from
crowds. We begin by describing an EM algorithm for jointly learning the
parameters of the network and the reliabilities of the annotators. Then, a
novel general-purpose crowd layer is proposed, which allows us to train deep
neural networks end-to-end, directly from the noisy labels of multiple
annotators, using only backpropagation. We empirically show that the proposed
approach is able to internally capture the reliability and biases of different
annotators and achieve new state-of-the-art results for various crowdsourced
datasets across different settings, namely classification, regression and
sequence labeling.Comment: 10 pages, The Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI), 201
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods
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