A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation

• URL: http://arxiv.org/abs/2110.02739v1
• Date: Tue, 28 Sep 2021 13:50:21 GMT
• Title: A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
• Authors: Jonathan Sadeghi, Blaine Rogers, James Gunn, Thomas Saunders, Sina Samangooei, Puneet Kumar Dokania, John Redford
• Abstract summary: We propose an approach that enables efficient large-scale testing using simplified low-fidelity simulators. Our approach relies on designing an efficient surrogate model corresponding to the compute intensive components of the task under test. We demonstrate the efficacy of our methodology by evaluating the performance of an autonomous driving task in the Carla simulator with reduced computational expense.
• Score: 5.4954641673299145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires large-scale testing of the model that can be extremely computationally expensive for complex real-world tasks. For example, tasks involving compute intensive object detectors as one of their components. In this work, we propose an approach that enables efficient large-scale testing using simplified low-fidelity simulators and without the computational cost of executing expensive deep learning models. Our approach relies on designing an efficient surrogate model corresponding to the compute intensive components of the task under test. We demonstrate the efficacy of our methodology by evaluating the performance of an autonomous driving task in the Carla simulator with reduced computational expense by training efficient surrogate models for PIXOR and CenterPoint LiDAR detectors, whilst demonstrating that the accuracy of the simulation is maintained.

Related papers

• Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs [76.43407125275202]
  o1-like models can emulate human-like long-time thinking during inference. This paper presents the first comprehensive study on the prevalent issue of overthinking in these models. We propose strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy.
  arXiv  Detail & Related papers  (2024-12-30T18:55:12Z)
• Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference [12.019504660711231]
  We introduce sequential neural posterior estimation (ASNPE) ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model. Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network.
  arXiv  Detail & Related papers  (2024-12-07T08:57:26Z)
• One-step Noisy Label Mitigation [86.57572253460125]
  Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical. We propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-02T18:42:56Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning [61.3506230781327]
  In robotics, one approach to generate training data builds on simulations based on dynamics models derived from first principles. Here, we leverage the imbalance in complexity of the dynamics to learn more sample-efficiently. We validate our method on several challenging simulated tasks and demonstrate that it improves learning both alone and when combined with an existing hindsight algorithm.
  arXiv  Detail & Related papers  (2023-03-03T21:55:04Z)
• Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression [0.6021787236982659]
  We propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model. By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling.
  arXiv  Detail & Related papers  (2022-11-16T02:31:57Z)
• Benchmarking Learning Efficiency in Deep Reservoir Computing [23.753943709362794]
  We introduce a benchmark of increasingly difficult tasks together with a data efficiency metric to measure how quickly machine learning models learn from training data. We compare the learning speed of some established sequential supervised models, such as RNNs, LSTMs, or Transformers, with relatively less known alternative models based on reservoir computing.
  arXiv  Detail & Related papers  (2022-09-29T08:16:52Z)
• Accelerated Policy Learning with Parallel Differentiable Simulation [59.665651562534755]
  We present a differentiable simulator and a new policy learning algorithm (SHAC) Our algorithm alleviates problems with local minima through a smooth critic function. We show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms.
  arXiv  Detail & Related papers  (2022-04-14T17:46:26Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits [0.16311150636417257]
  Machine learning models are used to predict accurate per-instance Functional De-Rating data for the full list of circuit instances. The presented methodology is applied on a practical example and various machine learning models are evaluated and compared.
  arXiv  Detail & Related papers  (2020-02-18T18:38:54Z)
• On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques [0.16311150636417257]
  De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements. New approach is proposed which uses Machine Learning to estimate the Functional De-Rating of individual flip-flops.
  arXiv  Detail & Related papers  (2020-02-18T15:18:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.