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Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation (2409.17085v1)

Published 25 Sep 2024 in cs.CV and stat.ML

Abstract: State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertainty quantification. While Bayesian neural networks provide a conceptually simple approach to serve those requirements, they suffer from the high dimensionality of the parameter space. Parameter-efficient fine-tuning (PEFT) methods, in particular low-rank adaptations (LoRA), have emerged as a popular strategy for adapting large-scale models to down-stream tasks by performing parameter inference on lower-dimensional subspaces. In this work, we investigate the suitability of PEFT methods for subspace Bayesian inference in large-scale Transformer-based vision models. We show that, indeed, combining BitFit, DiffFit, LoRA, and CoLoRA, a novel LoRA-inspired PEFT method, with Bayesian inference enables more robust and reliable predictive performance in MDE.

Summary

  • The paper demonstrates that integrating PEFT methods with Bayesian inference enhances predictive performance in monocular depth estimation tasks.
  • It introduces CoLoRA, a convolution-specific adaptation of LoRA, to achieve effective uncertainty quantification with low computational overhead.
  • Experimental results on NYU and KITTI datasets reveal improved calibration and reliability, critical for safety-critical applications.

Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation

The paper investigates the integration of Parameter-efficient Fine-Tuning (PEFT) methods within Bayesian neural networks to address monocular depth estimation (MDE) tasks using large-scale Transformer-based models. The principle objective is to maintain robust predictive performance while enabling comprehensive uncertainty quantification, a critical requirement in safety-critical applications such as autonomous driving or healthcare.

Context and Motivation

Recent advancements in computer vision and natural language processing have largely been propelled by expansive, self-supervised models trained on vast amounts of unlabeled data. Notable models within this ecosystem have demonstrated mitigated performance degradation under distributional shifts. However, their efficacy in domains requiring uncertainty estimation remains inadequately explored. Bayesian deep learning, with its potential for epistemic uncertainty quantification, presents an attractive yet computationally intensive solution due to the high-dimensional parameter space of these large models.

PEFT methods, particularly low-rank adaptations (LoRA), BitFit, and DiffFit, offer promising avenues for adapting large-scale models to downstream tasks by constraining parameter updates to lower-dimensional subspaces. This raises an intriguing question: can these subspaces also support cost-effective yet effective Bayesian inference?

Contributions

The paper evaluates the efficacy of integrating PEFT methodologies like LoRA, BitFit, DiffFit, and the novel CoLoRA — a convolution-specific extension of LoRA based on Tucker decompositions — for Bayesian inference in state-of-the-art depth estimation models. Central to this exploration are methodologies like Stochastic Weight Averaging Gaussians (SWAG) and checkpoint ensembles, which offer divergent approaches to approximating posterior distributions.

Methodological Approach

  1. Bayesian Deep Learning: The paper leverages Bayesian inference principles, converting point estimates of model parameters into distributions representing uncertainty. This encompasses computing posterior distributions and using Monte-Carlo sampling for predictions.
  2. SWAG: Implements SWAG and checkpoint ensembles to approximate posterior distributions. The approach involves reshaping and averaging checkpoints to derive Gaussian distributions (for SWAG) or treating checkpoints as posterior samples (for checkpoint ensembles).
  3. PEFT Methods: Introduces a variety of PEFT methods tailored for large vision models. The most notable contribution is CoLoRA, which adapts LoRA principles to convolutional kernels using Tucker decompositions, effectively capturing low-rank perturbations.

Experimental Results

Experiments were conducted on established depth estimation benchmarks, specifically the NYU and KITTI datasets. The findings indicate significant improvements in predictive performance and uncertainty calibration when applying Bayesian inference to PEFT subspaces.

  1. Predictive Performance: The negative log-likelihood (NLL) evaluations depict enhancements in predictive reliability across all PEFT methodologies. CoLoRA, particularly at higher ranks, bridges the gap between deterministic methods and full Bayesian inference on the entire parameter space. Notably, checkpoint ensembles yield superior performance, suggesting that even with limited sampling (as in DeepEns), substantial gains in model reliability can be achieved.
  2. Calibration: Calibration experiments reveal that PEFT-based Bayesian models deliver well-calibrated uncertainty estimates, effectively prioritizing predictions with higher certainty margins. CoLoRA, despite its novel application, presents competitive performance across different quantiles, sometimes surpassing traditional methods.

Implications and Future Directions

The integration of PEFT methods into Bayesian neural networks substantiates a trade-off between computational efficiency and model reliability. This has significant implications for deploying large vision models in resource-constrained, safety-critical settings. Future directions might explore the generalization of CoLoRA across different computer vision tasks and models, its theoretical underpinnings in Bayesian optimization, and an in-depth comparative analysis with other posterior approximation techniques like Variational Inference or Hamiltonian Monte Carlo.

Conclusion

The paper successfully highlights the synergistic potential of combining parameter-efficient fine-tuning with Bayesian inference in the context of monocular depth estimation. The proposed CoLoRA method, along with detailed evaluations involving SWAG and checkpoint ensembles, provides a comprehensive framework for introducing uncertainty quantification in large-scale vision models without incurring substantial computational overheads.

Considering the demonstrated improvements in predictive performance and uncertainty calibration, these findings are pivotal for advancing the deployment of foundation models in domains where reliability and robustness are paramount. Further exploration and refinement of these techniques could yield broader applicability across various AI fields, potentially reshaping the paradigm of model fine-tuning and inference.

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