66 research outputs found
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In the context of real-world object recognition
applications (e.g., robotic vision), where continuous learning is crucial, very
few datasets and benchmarks are available to evaluate and compare emerging
techniques. In this work we propose a new dataset and benchmark CORe50,
specifically designed for continuous object recognition, and introduce baseline
approaches for different continuous learning scenarios
Semi-supervised Tuning from Temporal Coherence
Recent works demonstrated the usefulness of temporal coherence to regularize
supervised training or to learn invariant features with deep architectures. In
particular, enforcing smooth output changes while presenting temporally-closed
frames from video sequences, proved to be an effective strategy. In this paper
we prove the efficacy of temporal coherence for semi-supervised incremental
tuning. We show that a deep architecture, just mildly trained in a supervised
manner, can progressively improve its classification accuracy, if exposed to
video sequences of unlabeled data. The extent to which, in some cases, a
semi-supervised tuning allows to improve classification accuracy (approaching
the supervised one) is somewhat surprising. A number of control experiments
pointed out the fundamental role of temporal coherence.Comment: Under review as a conference paper at ICLR 201
Continual Reinforcement Learning in 3D Non-stationary Environments
High-dimensional always-changing environments constitute a hard challenge for
current reinforcement learning techniques. Artificial agents, nowadays, are
often trained off-line in very static and controlled conditions in simulation
such that training observations can be thought as sampled i.i.d. from the
entire observations space. However, in real world settings, the environment is
often non-stationary and subject to unpredictable, frequent changes. In this
paper we propose and openly release CRLMaze, a new benchmark for learning
continually through reinforcement in a complex 3D non-stationary task based on
ViZDoom and subject to several environmental changes. Then, we introduce an
end-to-end model-free continual reinforcement learning strategy showing
competitive results with respect to four different baselines and not requiring
any access to additional supervised signals, previously encountered
environmental conditions or observations.Comment: Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5
table
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
Making applications aware of the mobility experienced by the user can open
the door to a wide range of novel services in different use-cases, from smart
parking to vehicular traffic monitoring. In the literature, there are many
different studies demonstrating the theoretical possibility of performing
Transportation Mode Detection (TMD) by mining smart-phones embedded sensors
data. However, very few of them provide details on the benchmarking process and
on how to implement the detection process in practice. In this study, we
provide guidelines and fundamental results that can be useful for both
researcher and practitioners aiming at implementing a working TMD system. These
guidelines consist of three main contributions. First, we detail the
construction of a training dataset, gathered by heterogeneous users and
including five different transportation modes; the dataset is made available to
the research community as reference benchmark. Second, we provide an in-depth
analysis of the sensor-relevance for the case of Dual TDM, which is required by
most of mobility-aware applications. Third, we investigate the possibility to
perform TMD of unknown users/instances not present in the training set and we
compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context
and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece,
March 19-23, 201
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Robotic vision is a field where continual learning can play a significant
role. An embodied agent operating in a complex environment subject to frequent
and unpredictable changes is required to learn and adapt continuously. In the
context of object recognition, for example, a robot should be able to learn
(without forgetting) objects of never before seen classes as well as improving
its recognition capabilities as new instances of already known classes are
discovered. Ideally, continual learning should be triggered by the availability
of short videos of single objects and performed on-line on on-board hardware
with fine-grained updates. In this paper, we introduce a novel continual
learning protocol based on the CORe50 benchmark and propose two rehearsal-free
continual learning techniques, CWR* and AR1*, that can learn effectively even
in the challenging case of nearly 400 small non-i.i.d. incremental batches. In
particular, our experiments show that AR1* can outperform other
state-of-the-art rehearsal-free techniques by more than 15% accuracy in some
cases, with a very light and constant computational and memory overhead across
training batches.Comment: Accepted in the CLVision Workshop at CVPR2020: 12 pages, 7 figures, 5
tables, 3 algorithm
In-context Interference in Chat-based Large Language Models
Large language models (LLMs) have had a huge impact on society due to their
impressive capabilities and vast knowledge of the world. Various applications
and tools have been created that allow users to interact with these models in a
black-box scenario. However, one limitation of this scenario is that users
cannot modify the internal knowledge of the model, and the only way to add or
modify internal knowledge is by explicitly mentioning it to the model during
the current interaction. This learning process is called in-context training,
and it refers to training that is confined to the user's current session or
context. In-context learning has significant applications, but also has
limitations that are seldom studied. In this paper, we present a study that
shows how the model can suffer from interference between information that
continually flows in the context, causing it to forget previously learned
knowledge, which can reduce the model's performance. Along with showing the
problem, we propose an evaluation benchmark based on the bAbI dataset
Continual Learning with Deep Architectures
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills.
However, despite early speculations and few pioneering works, very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for. Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous
intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
In this dissertation, we study the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI. We propose a comprehensive and unifying framework for continual learning, new metrics, benchmarks and algorithms, as well as providing substantial experimental evaluations in different supervised, unsupervised and reinforcement learning tasks
Latent Replay for Real-Time Continual Learning
Training deep neural networks at the edge on light computational devices,
embedded systems and robotic platforms is nowadays very challenging. Continual
learning techniques, where complex models are incrementally trained on small
batches of new data, can make the learning problem tractable even for CPU-only
embedded devices enabling remarkable levels of adaptiveness and autonomy.
However, a number of practical problems need to be solved: catastrophic
forgetting before anything else. In this paper we introduce an original
technique named "Latent Replay" where, instead of storing a portion of past
data in the input space, we store activations volumes at some intermediate
layer. This can significantly reduce the computation and storage required by
native rehearsal. To keep the representation stable and the stored activations
valid we propose to slow-down learning at all the layers below the latent
replay one, leaving the layers above free to learn at full pace. In our
experiments we show that Latent Replay, combined with existing continual
learning techniques, achieves state-of-the-art performance on complex video
benchmarks such as CORe50 NICv2 (with nearly 400 small and highly non-i.i.d.
batches) and OpenLORIS. Finally, we demonstrate the feasibility of nearly
real-time continual learning on the edge through the deployment of the proposed
technique on a smartphone device.Comment: Pre-print v3: 13 pages, 9 figures, 10 tables, 1 algorith
- …