How to Train Your Energy-Based Model for Regression (2005.01698v2)

Abstract: Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking. Training EBMs is however known to be challenging. While a variety of different techniques have been explored for generative modeling, the application of EBMs to regression is not a well-studied problem. How EBMs should be trained for best possible regression performance is thus currently unclear. We therefore accept the task of providing the first detailed study of this problem. To that end, we propose a simple yet highly effective extension of noise contrastive estimation, and carefully compare its performance to six popular methods from literature on the tasks of 1D regression and object detection. The results of this comparison suggest that our training method should be considered the go-to approach. We also apply our method to the visual tracking task, achieving state-of-the-art performance on five datasets. Notably, our tracker achieves 63.7% AUC on LaSOT and 78.7% Success on TrackingNet. Code is available at https://github.com/fregu856/ebms_regression.

• The paper extends Noise Contrastive Estimation (NCE) with NCE+ to effectively model and reduce annotation noise in regression tasks.
• It provides a comprehensive comparison of NCE+ against six established techniques, demonstrating superior stability and lower KL divergence in low-sample settings.
• The approach achieves state-of-the-art performance in visual tracking, with notable improvements on the LaSOT and TrackingNet benchmarks.

On Training Energy-Based Models for Regression

The paper "How to Train Your Energy-Based Model for Regression" explores the intricacies of employing Energy-Based Models (EBMs) for regression tasks, a less explored domain as compared to their widespread use in generative modeling. Grounded in recent advancements, the authors address the challenge of training EBMs for regression, where the integration over continuous target spaces and the inherent intractability of the normalization constant pose significant obstacles.

Key Contributions

1. Extension of Noise Contrastive Estimation (NCE): The authors propose an augmentation to the conventional NCE approach, termed NCE+. This method extends NCE by accounting for noise in the annotation process, thereby enhancing the robustness of the model training. The introduction of NCE+ addresses the need for modeling label noise which is often prevalent in real-world datasets.
2. Comprehensive Comparison of Training Techniques: The paper meticulously compares NCE+ against six established methods—ML with Importance Sampling (ML-IS), KL Divergence with Importance Sampling (KLD-IS), ML with MCMC (ML-MCMC), standard NCE, Score Matching (SM), and Denoising Score Matching (DSM). This comparative evaluation focuses on both synthetic 1D regression tasks and practical applications like object detection.
3. Performance on Visual Tracking: The practical efficacy of NCE+ is demonstrated through its application to the task of visual tracking, where the method surpasses contemporary approaches on multiple datasets. Notable benchmarks include achieving state-of-the-art performance with 63.7% AUC on the LaSOT dataset and 78.7% Success on TrackingNet.

Numerical Findings and Comparative Analysis

The experimental results reveal that NCE+ not only competes effectively with but often surpasses other methods, particularly in scenarios requiring minimal sample sizes ($M$). On one-dimensional regression tasks, NCE+ shows superior performance with a lower KL divergence compared to methods like ML-IS and KLD-IS. The robustness of NCE+ to numerical stability issues is a strong point, especially highlighted in experiments showcasing its stability in low-sample ($M=1$) regimes.

In object detection, the NCE+ method achieves a notable improvement in the Average Precision (AP) and alleviates computational burdens associated with other methods like ML-MCMC, which demand significantly higher computational resources due to extensive Langevin dynamics steps.

Implications and Future Directions

The adoption of NCE+, with its built-in capacity to manage annotation noise, suggests a promising trajectory for EBMs in applications characterized by imperfect data. Such methodological enhancements could be pivotal for disciplines relying on noisy or ambiguous real-world data inputs.

In the broader context of machine learning, the insights presented in the paper underscore the necessity for tailored training procedures when leveraging EBMs for tasks outside traditional generative modeling. The adaptability of NCE+ has the potential to spur further exploration into EBMs as competitive alternatives in regression, encouraging future research to investigate scalability and adaption to other domains such as time-series forecasting and anomaly detection.

The confirmation of EBMs' applicability to regression, with the proposed enhancements, posits NCE+ as a foundational building block for future model architectures. Prospective studies could aim to refine these techniques for higher-dimensional regression tasks and explore the incorporation of uncertain labels in a more principled manner, enhancing generalizability to even more varied domains in machine learning.