75,595 research outputs found
Deep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced functions. We provide comprehensive empirical evidence
showing that these residual networks are easier to optimize, and can gain
accuracy from considerably increased depth. On the ImageNet dataset we evaluate
residual nets with a depth of up to 152 layers---8x deeper than VGG nets but
still having lower complexity. An ensemble of these residual nets achieves
3.57% error on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100
and 1000 layers.
The depth of representations is of central importance for many visual
recognition tasks. Solely due to our extremely deep representations, we obtain
a 28% relative improvement on the COCO object detection dataset. Deep residual
nets are foundations of our submissions to ILSVRC & COCO 2015 competitions,
where we also won the 1st places on the tasks of ImageNet detection, ImageNet
localization, COCO detection, and COCO segmentation.Comment: Tech repor
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
Multi-learner based recursive supervised training
In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information is available along with the graph data. For example, for
neurological disorder identification, in addition to the brain networks derived
from neuroimaging data, hundreds of clinical, immunologic, serologic and
cognitive measures may also be documented for each subject. These measures
compose multiple side views encoding a tremendous amount of supplemental
information for diagnostic purposes, yet are often ignored. In this paper, we
study the problem of discriminative subgraph selection using multiple side
views and propose a novel solution to find an optimal set of subgraph features
for graph classification by exploring a plurality of side views. We derive a
feature evaluation criterion, named gSide, to estimate the usefulness of
subgraph patterns based upon side views. Then we develop a branch-and-bound
algorithm, called gMSV, to efficiently search for optimal subgraph features by
integrating the subgraph mining process and the procedure of discriminative
feature selection. Empirical studies on graph classification tasks for
neurological disorders using brain networks demonstrate that subgraph patterns
selected by the multi-side-view guided subgraph selection approach can
effectively boost graph classification performances and are relevant to disease
diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM)
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