Equivariant Adaptation of Large Pretrained Models (2310.01647v2)

Abstract: Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to higher sample efficiency and more accurate and robust predictions. However, redesigning each component of prevalent deep neural network architectures to achieve chosen equivariance is a difficult problem and can result in a computationally expensive network during both training and inference. A recently proposed alternative towards equivariance that removes the architectural constraints is to use a simple canonicalization network that transforms the input to a canonical form before feeding it to an unconstrained prediction network. We show here that this approach can effectively be used to make a large pretrained network equivariant. However, we observe that the produced canonical orientations can be misaligned with those of the training distribution, hindering performance. Using dataset-dependent priors to inform the canonicalization function, we are able to make large pretrained models equivariant while maintaining their performance. This significantly improves the robustness of these models to deterministic transformations of the data, such as rotations. We believe this equivariant adaptation of large pretrained models can help their domain-specific applications with known symmetry priors.

• The paper demonstrates that integrating canonicalization functions enhances zero-shot segmentation, improving mAP in models such as SAM on COCO 2017.
• It reveals significant classification gains on datasets like CIFAR10 using C8-augmented canonicalization and Equivariant Self-Supervised Learning.
• The study highlights that the added preprocessing layer reduces model variance and increases robustness with minimal computational overhead.

Evaluating Canonicalization Functions in Large Pre-trained Segmentation and Classification Models

This paper investigates the impact of integrating canonicalization functions into large-scale pre-trained neural networks, with a focus on both image segmentation and classification tasks. The primary aim is to assess whether these canonicalization techniques can enhance zero-shot and fine-tuning performances across different datasets and network architectures.

Methodology and Experimental Setup

The paper contrasts conventional large pre-trained networks with those augmented with trained canonicalization functions. Specifically, the experiments are conducted on MaskRCNN and SAM segmentation models as well as ResNet50 and ViT for classification tasks. Canonicalization functions included Prior-Regularized Local Canonicalization (LC) and ConvNet Canonicalizers, each bringing distinct design principles to the task of harmonizing input data representation before prediction.

The researchers evaluated zero-shot performance on the COCO 2017 dataset, which is a standard benchmark for object detection and segmentation. For classification, experiments were conducted with CIFAR10, CIFAR100, and STL10 datasets, allowing exploration of canonicalization effects across distinct data types.

Key Findings

• Segmentation Performance: On the COCO 2017 dataset, large-scale pre-trained models such as MaskRCNN and SAM showed improvement with canonicalization. For instance, in the zero-shot setup, SAM exhibited a mean Average Precision (mAP) increase from 58.78 to 62.13 when integrated with the Prior-Regularized LC.
• Classification Performance: When models were evaluated with and without canonicalization, significant performance gains were observed. Notably, on the CIFAR10 dataset, models trained with $C8$-augmented canonicalization achieved mAP enhancements, bolstering their generalization capacity by adapting to rotational variances.
• Impact of Equivariant Self-Supervised Learning (E-SSL): Pre-trained ResNet50 models with E-SSL also demonstrated notable improvements when augmented with Prior-Regularized LC, as reflected in the $C8$-averaged accuracy outperforming the vanilla baseline.

Theoretical and Practical Implications

The integration of canonicalization functions introduces an additional preprocessing layer that aids in the normalization of data representations before feeding into the prediction network. This approach aligns with the theory that enhanced input representation can reduce model variance and potentially increase robustness to unseen data distributions. Practically, it offers an avenue for improving network performances without necessitating extensive additional parameters, thus maintaining computational efficiency.

Future Directions

The paper suggests several pathways for further research. First, extending canonicalization strategies to domains beyond vision, such as natural language processing or multimodal tasks, could validate the versatility of these approaches. Additionally, exploring the interplay between different canonicalization functions and model architectures might uncover new synergies, allowing for more tailored applications based on task specifics.

Overall, this paper contributes valuable insights into the refinement of pre-trained models via canonicalization techniques, emphasizing the importance of holistic input data approaches in model performance augmentation.