17 research outputs found
Contrastive Learning for Lane Detection via Cross-Similarity
Detecting road lanes is challenging due to intricate markings vulnerable to
unfavorable conditions. Lane markings have strong shape priors, but their
visibility is easily compromised. Factors like lighting, weather, vehicles,
pedestrians, and aging colors challenge the detection. A large amount of data
is required to train a lane detection approach that can withstand natural
variations caused by low visibility. This is because there are numerous lane
shapes and natural variations that exist. Our solution, Contrastive Learning
for Lane Detection via cross-similarity (CLLD), is a self-supervised learning
method that tackles this challenge by enhancing lane detection models
resilience to real-world conditions that cause lane low visibility. CLLD is a
novel multitask contrastive learning that trains lane detection approaches to
detect lane markings even in low visible situations by integrating local
feature contrastive learning (CL) with our new proposed operation
cross-similarity. Local feature CL focuses on extracting features for small
image parts, which is necessary to localize lane segments, while
cross-similarity captures global features to detect obscured lane segments
using their surrounding. We enhance cross-similarity by randomly masking parts
of input images for augmentation. Evaluated on benchmark datasets, CLLD
outperforms state-of-the-art contrastive learning, especially in
visibility-impairing conditions like shadows. Compared to supervised learning,
CLLD excels in scenarios like shadows and crowded scenes.Comment: 10 page
PR-DARTS: Pruning-Based Differentiable Architecture Search
The deployment of Convolutional Neural Networks (CNNs) on edge devices is
hindered by the substantial gap between performance requirements and available
processing power. While recent research has made large strides in developing
network pruning methods for reducing the computing overhead of CNNs, there
remains considerable accuracy loss, especially at high pruning ratios.
Questioning that the architectures designed for non-pruned networks might not
be effective for pruned networks, we propose to search architectures for
pruning methods by defining a new search space and a novel search objective. To
improve the generalization of the pruned networks, we propose two novel
PrunedConv and PrunedLinear operations. Specifically, these operations mitigate
the problem of unstable gradients by regularizing the objective function of the
pruned networks. The proposed search objective enables us to train architecture
parameters regarding the pruned weight elements. Quantitative analyses
demonstrate that our searched architectures outperform those used in the
state-of-the-art pruning networks on CIFAR-10 and ImageNet. In terms of
hardware effectiveness, PR-DARTS increases MobileNet-v2's accuracy from 73.44%
to 81.35% (+7.91% improvement) and runs 3.87 faster.Comment: 18 pages with 11 figure
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it as \textit{Model Transfer Reinforcement Learning (MTRL)} problem. First, we formulate MTRL for discrete MDPs and Linear Quadratic Regulators (LQRs) with continuous state actions. Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings. In the first stage, MLEMTRL uses a \textit{constrained Maximum Likelihood Estimation (MLE)}-based approach to estimate the target MDP model using a set of known MDP models. In the second stage, using the estimated target MDP model, MLEMTRL deploys a model-based planning algorithm appropriate for the MDP class. Theoretically, we prove worst-case regret bounds for MLEMTRL both in realisable and non-realisable settings. We empirically demonstrate that MLEMTRL allows faster learning in new MDPs than learning from scratch and achieves near-optimal performance depending on the similarity of the available MDPs and the target MDP
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
The ubiquity of camera-enabled devices has led to large amounts of unlabeled
image data being produced at the edge. The integration of self-supervised
learning (SSL) and federated learning (FL) into one coherent system can
potentially offer data privacy guarantees while also advancing the quality and
robustness of the learned visual representations without needing to move data
around. However, client bias and divergence during FL aggregation caused by
data heterogeneity limits the performance of learned visual representations on
downstream tasks. In this paper, we propose a new aggregation strategy termed
Layer-wise Divergence Aware Weight Aggregation (L-DAWA) to mitigate the
influence of client bias and divergence during FL aggregation. The proposed
method aggregates weights at the layer-level according to the measure of
angular divergence between the clients' model and the global model. Extensive
experiments with cross-silo and cross-device settings on CIFAR-10/100 and Tiny
ImageNet datasets demonstrate that our methods are effective and obtain new
SOTA performance on both contrastive and non-contrastive SSL approaches
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Existing datasets for autonomous driving (AD) often lack diversity and
long-range capabilities, focusing instead on 360{\deg} perception and temporal
reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a
large-scale and diverse multimodal dataset collected over two years in various
European countries, covering an area 9x that of existing datasets. ZOD boasts
the highest range and resolution sensors among comparable datasets, coupled
with detailed keyframe annotations for 2D and 3D objects (up to 245m), road
instance/semantic segmentation, traffic sign recognition, and road
classification. We believe that this unique combination will facilitate
breakthroughs in long-range perception and multi-task learning. The dataset is
composed of Frames, Sequences, and Drives, designed to encompass both data
diversity and support for spatio-temporal learning, sensor fusion,
localization, and mapping. Frames consist of 100k curated camera images with
two seconds of other supporting sensor data, while the 1473 Sequences and 29
Drives include the entire sensor suite for 20 seconds and a few minutes,
respectively. ZOD is the only large-scale AD dataset released under a
permissive license, allowing for both research and commercial use. More
information, and an extensive devkit, can be found at https://zod.zenseact.comComment: International Conference on Computer Vision (ICCV) 202
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
In this paper, we consider risk-sensitive sequential decision-making in
model-based Reinforcement Learning (RL). Our contributions are two-fold. First,
we introduce a novel and coherent quantification of risk, namely composite
risk, which quantifies joint effect of aleatory and epistemic risk during the
learning process. Existing works considered either aleatory or epistemic risk
individually, or an additive combination of the two. We prove that the additive
formulation is a particular case of the composite risk when the epistemic risk
measure is replaced with expectation. Thus, the composite risk provides an
estimate more sensitive to both aleatory and epistemic sources of uncertainties
than the individual and additive formulations. Following that, we propose to
use a bootstrapping method, SENTINEL-K, for performing distributional RL.
SENTINEL-K uses an ensemble of learners to estimate the return
distribution. We use the Follow The Regularised Leader (FTRL) to aggregate the
return distributions of learners and to estimate the composite risk. We
experimentally verify that SENTINEL-K estimates the return distribution better,
and while used with composite risk estimate, demonstrates better risk-sensitive
performance than state-of-the-art risk-sensitive and distributional RL
algorithms.Comment: 30 pages, 9 figures, 8 table