39,421 research outputs found
Committee-Based Sample Selection for Probabilistic Classifiers
In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger
Active learning with gaussian processes for object categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) are powerful regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. The uncertainty model provided by GPs offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We derive a novel active category learning method based on our probabilistic regression model, and show that a significant boost in classification performance is possible, especially when the amount of training data for a category is ultimately very small. 1
GOGGLES: Automatic Image Labeling with Affinity Coding
Generating large labeled training data is becoming the biggest bottleneck in
building and deploying supervised machine learning models. Recently, the data
programming paradigm has been proposed to reduce the human cost in labeling
training data. However, data programming relies on designing labeling functions
which still requires significant domain expertise. Also, it is prohibitively
difficult to write labeling functions for image datasets as it is hard to
express domain knowledge using raw features for images (pixels).
We propose affinity coding, a new domain-agnostic paradigm for automated
training data labeling. The core premise of affinity coding is that the
affinity scores of instance pairs belonging to the same class on average should
be higher than those of pairs belonging to different classes, according to some
affinity functions. We build the GOGGLES system that implements affinity coding
for labeling image datasets by designing a novel set of reusable affinity
functions for images, and propose a novel hierarchical generative model for
class inference using a small development set.
We compare GOGGLES with existing data programming systems on 5 image labeling
tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a
minimum of 71% to a maximum of 98% without requiring any extensive human
annotation. In terms of end-to-end performance, GOGGLES outperforms the
state-of-the-art data programming system Snuba by 21% and a state-of-the-art
few-shot learning technique by 5%, and is only 7% away from the fully
supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management
of Dat
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
Paucity of large curated hand-labeled training data for every
domain-of-interest forms a major bottleneck in the deployment of machine
learning models in computer vision and other fields. Recent work (Data
Programming) has shown how distant supervision signals in the form of labeling
functions can be used to obtain labels for given data in near-constant time. In
this work, we present Adversarial Data Programming (ADP), which presents an
adversarial methodology to generate data as well as a curated aggregated label
has given a set of weak labeling functions. We validated our method on the
MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many
state-of-the-art models. We conducted extensive experiments to study its
usefulness, as well as showed how the proposed ADP framework can be used for
transfer learning as well as multi-task learning, where data from two domains
are generated simultaneously using the framework along with the label
information. Our future work will involve understanding the theoretical
implications of this new framework from a game-theoretic perspective, as well
as explore the performance of the method on more complex datasets.Comment: CVPR 2018 main conference pape
Unsupervised learning of human motion
An unsupervised learning algorithm that can obtain a probabilistic model of an object composed of a collection of parts (a moving human body in our examples) automatically from unlabeled training data is presented. The training data include both useful "foreground" features as well as features that arise from irrelevant background clutter - the correspondence between parts and detected features is unknown. The joint probability density function of the parts is represented by a mixture of decomposable triangulated graphs which allow for fast detection. To learn the model structure as well as model parameters, an EM-like algorithm is developed where the labeling of the data (part assignments) is treated as hidden variables. The unsupervised learning technique is not limited to decomposable triangulated graphs. The efficiency and effectiveness of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled image sequences, and testing the learned models on a variety of sequences
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