15 research outputs found
Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
With the increasing volume of data in the world, the best approach for
learning from this data is to exploit an online learning algorithm. Online
ensemble methods are online algorithms which take advantage of an ensemble of
classifiers to predict labels of data. Prediction with expert advice is a
well-studied problem in the online ensemble learning literature. The Weighted
Majority algorithm and the randomized weighted majority (RWM) are the most
well-known solutions to this problem, aiming to converge to the best expert.
Since among some expert, the best one does not necessarily have the minimum
error in all regions of data space, defining specific regions and converging to
the best expert in each of these regions will lead to a better result. In this
paper, we aim to resolve this defect of RWM algorithms by proposing a novel
online ensemble algorithm to the problem of prediction with expert advice. We
propose a cascading version of RWM to achieve not only better experimental
results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure
One-Shot Learning for Semantic Segmentation
Low-shot learning methods for image classification support learning from
sparse data. We extend these techniques to support dense semantic image
segmentation. Specifically, we train a network that, given a small set of
annotated images, produces parameters for a Fully Convolutional Network (FCN).
We use this FCN to perform dense pixel-level prediction on a test image for the
new semantic class. Our architecture shows a 25% relative meanIoU improvement
compared to the best baseline methods for one-shot segmentation on unseen
classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.Comment: To appear in the proceedings of the British Machine Vision Conference
(BMVC) 2017. The code is available at https://github.com/lzzcd001/OSLS
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA)
201
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised
Domain Adaptation (UDA) method for LiDAR segmentation. Existing self-training
methods use a model trained on labeled source data to generate pseudo labels
for target data and refine the predictions via fine-tuning the network on the
pseudo labels. These methods suffer from domain shifts caused by different
LiDAR sensor configurations in the source and target domains. We propose two
techniques to reduce sensor discrepancy and improve pseudo label quality: 1)
LiDAR beam subsampling, which simulates different LiDAR scanning patterns by
randomly dropping beams; 2) cross-frame ensembling, which exploits temporal
consistency of consecutive frames to generate more reliable pseudo labels. Our
method is simple, generalizable, and does not incur any extra inference cost.
We evaluate our method on several public LiDAR datasets and show that it
outperforms the state-of-the-art methods by more than mIoU on average
for all scenarios. Code will be available at
https://github.com/JHLee0513/LiDARUDA.Comment: Accepted ICCV 2023 (Oral
CAFA: Class-Aware Feature Alignment for Test-Time Adaptation
Despite recent advancements in deep learning, deep neural networks continue
to suffer from performance degradation when applied to new data that differs
from training data. Test-time adaptation (TTA) aims to address this challenge
by adapting a model to unlabeled data at test time. TTA can be applied to
pretrained networks without modifying their training procedures, enabling them
to utilize a well-formed source distribution for adaptation. One possible
approach is to align the representation space of test samples to the source
distribution (\textit{i.e.,} feature alignment). However, performing feature
alignment in TTA is especially challenging in that access to labeled source
data is restricted during adaptation. That is, a model does not have a chance
to learn test data in a class-discriminative manner, which was feasible in
other adaptation tasks (\textit{e.g.,} unsupervised domain adaptation) via
supervised losses on the source data. Based on this observation, we propose a
simple yet effective feature alignment loss, termed as Class-Aware Feature
Alignment (CAFA), which simultaneously 1) encourages a model to learn target
representations in a class-discriminative manner and 2) effectively mitigates
the distribution shifts at test time. Our method does not require any
hyper-parameters or additional losses, which are required in previous
approaches. We conduct extensive experiments on 6 different datasets and show
our proposed method consistently outperforms existing baselines