24 research outputs found
Evaluating Pretrained models for Deployable Lifelong Learning
We create a novel benchmark for evaluating a Deployable Lifelong Learning
system for Visual Reinforcement Learning (RL) that is pretrained on a curated
dataset, and propose a novel Scalable Lifelong Learning system capable of
retaining knowledge from the previously learnt RL tasks. Our benchmark measures
the efficacy of a deployable Lifelong Learning system that is evaluated on
scalability, performance and resource utilization. Our proposed system, once
pretrained on the dataset, can be deployed to perform continual learning on
unseen tasks. Our proposed method consists of a Few Shot Class Incremental
Learning (FSCIL) based task-mapper and an encoder/backbone trained entirely
using the pretrain dataset. The policy parameters corresponding to the
recognized task are then loaded to perform the task. We show that this system
can be scaled to incorporate a large number of tasks due to the small memory
footprint and fewer computational resources. We perform experiments on our DeLL
(Deployment for Lifelong Learning) benchmark on the Atari games to determine
the efficacy of the system.Comment: In submission to CoLLA 2024. Also published in the Proceedings of
WACV 2024 Workshop on Pretrainin
Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets
AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches
CLR: Channel-wise Lightweight Reprogramming for Continual Learning
Continual learning aims to emulate the human ability to continually
accumulate knowledge over sequential tasks. The main challenge is to maintain
performance on previously learned tasks after learning new tasks, i.e., to
avoid catastrophic forgetting. We propose a Channel-wise Lightweight
Reprogramming (CLR) approach that helps convolutional neural networks (CNNs)
overcome catastrophic forgetting during continual learning. We show that a CNN
model trained on an old task (or self-supervised proxy task) could be
``reprogrammed" to solve a new task by using our proposed lightweight (very
cheap) reprogramming parameter. With the help of CLR, we have a better
stability-plasticity trade-off to solve continual learning problems: To
maintain stability and retain previous task ability, we use a common
task-agnostic immutable part as the shared ``anchor" parameter set. We then add
task-specific lightweight reprogramming parameters to reinterpret the outputs
of the immutable parts, to enable plasticity and integrate new knowledge. To
learn sequential tasks, we only train the lightweight reprogramming parameters
to learn each new task. Reprogramming parameters are task-specific and
exclusive to each task, which makes our method immune to catastrophic
forgetting. To minimize the parameter requirement of reprogramming to learn new
tasks, we make reprogramming lightweight by only adjusting essential kernels
and learning channel-wise linear mappings from anchor parameters to
task-specific domain knowledge. We show that, for general CNNs, the CLR
parameter increase is less than 0.6\% for any new task. Our method outperforms
13 state-of-the-art continual learning baselines on a new challenging sequence
of 53 image classification datasets. Code and data are available at
https://github.com/gyhandy/Channel-wise-Lightweight-ReprogrammingComment: ICCV 202
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
We focus on the challenge of out-of-distribution (OOD) detection in deep
learning models, a crucial aspect in ensuring reliability. Despite considerable
effort, the problem remains significantly challenging in deep learning models
due to their propensity to output over-confident predictions for OOD inputs. We
propose a novel one-class open-set OOD detector that leverages text-image
pre-trained models in a zero-shot fashion and incorporates various descriptions
of in-domain and OOD. Our approach is designed to detect anything not in-domain
and offers the flexibility to detect a wide variety of OOD, defined via fine-
or coarse-grained labels, or even in natural language. We evaluate our approach
on challenging benchmarks including large-scale datasets containing
fine-grained, semantically similar classes, distributionally shifted images,
and multi-object images containing a mixture of in-domain and OOD objects. Our
method shows superior performance over previous methods on all benchmarks. Code
is available at https://github.com/gyhandy/One-Class-AnythingComment: 16 pages (including appendix and references), 3 figure
Comprehensive Identification of Protein Substrates of the Dot/Icm Type IV Transporter of Legionella pneumophila
A large number of proteins transferred by the Legionella pneumophila Dot/Icm system have been identified by various strategies. With no exceptions, these strategies are based on one or more characteristics associated with the tested proteins. Given the high level of diversity exhibited by the identified proteins, it is possible that some substrates have been missed in these screenings. In this study, we took a systematic method to survey the L. pneumophila genome by testing hypothetical orfs larger than 300 base pairs for Dot/Icm-dependent translocation. 798 of the 832 analyzed orfs were successfully fused to the carboxyl end of β-lactamase. The transfer of the fusions into mammalian cells was determined using the β-lactamase reporter substrate CCF4-AM. These efforts led to the identification of 164 proteins positive in translocation. Among these, 70 proteins are novel substrates of the Dot/Icm system. These results brought the total number of experimentally confirmed Dot/Icm substrates to 275. Sequence analysis of the C-termini of these identified proteins revealed that Lpg2844, which contains few features known to be important for Dot/Icm-dependent protein transfer can be translocated at a high efficiency. Thus, our efforts have identified a large number of novel substrates of the Dot/Icm system and have revealed the diverse features recognizable by this protein transporter
Invariant Structure Learning for Better Generalization and Causal Explainability
Learning the causal structure behind data is invaluable for improving
generalization and obtaining high-quality explanations. We propose a novel
framework, Invariant Structure Learning (ISL), that is designed to improve
causal structure discovery by utilizing generalization as an indication. ISL
splits the data into different environments, and learns a structure that is
invariant to the target across different environments by imposing a consistency
constraint. An aggregation mechanism then selects the optimal classifier based
on a graph structure that reflects the causal mechanisms in the data more
accurately compared to the structures learnt from individual environments.
Furthermore, we extend ISL to a self-supervised learning setting where accurate
causal structure discovery does not rely on any labels. This self-supervised
ISL utilizes invariant causality proposals by iteratively setting different
nodes as targets. On synthetic and real-world datasets, we demonstrate that ISL
accurately discovers the causal structure, outperforms alternative methods, and
yields superior generalization for datasets with significant distribution
shifts.Comment: 16 pages (including Appendix), 4 figure