Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks (2505.06224v1)

Abstract: Downstream probing has been the dominant method for evaluating model representations, an important process given the increasing prominence of self-supervised learning and foundation models. However, downstream probing primarily assesses the availability of task-relevant information in the model's latent space, overlooking attributes such as equivariance, invariance, and disentanglement, which contribute to the interpretability, adaptability, and utility of representations in real-world applications. While some attempts have been made to measure these qualities in representations, no unified evaluation framework with modular, generalizable, and interpretable metrics exists. In this paper, we argue for the importance of representation evaluation beyond downstream probing. We introduce a standardized protocol to quantify informativeness, equivariance, invariance, and disentanglement of factors of variation in model representations. We use it to evaluate representations from a variety of models in the image and speech domains using different architectures and pretraining approaches on identified controllable factors of variation. We find that representations from models with similar downstream performance can behave substantially differently with regard to these attributes. This hints that the respective mechanisms underlying their downstream performance are functionally different, prompting new research directions to understand and improve representations.

Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks

The paper "Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks" presents a methodical approach to evaluating the quality and functionality of model representations, shifting the focus beyond the common downstream task probing. With the rise of self-supervised learning (SSL) and foundation models, the importance of representation evaluation has grown, yet current methods primarily assess task-relevant information and neglect intrinsic attributes like equivariance, invariance, and disentanglement. These attributes are argued to be crucial for enhancing the interpretability, adaptability, and real-world applicability of the models.

Research Approach

The authors introduce a standardized evaluation protocol that categorizes representation quality evaluation into four distinct axes: informativeness, equivariance, invariance, and disentanglement. This comprehensive framework allows for a detailed analysis of the behavior and structural organization of representations across various models beyond their performance on downstream tasks.

• Informativeness: This axis considers the absolute prediction accuracy of factors of variation (FVs) within a representation. It draws parallels to traditional downstream probing by inspecting how easily these FVs can be predicted from model representations.
• Equivariance: Equivariance measures the relationship between transformations in the input space and their corresponding effects in the latent space. The authors implement two equivariance tasks—parameter prediction and representation prediction—to quantify the extent to which transformations are captured within representations.
• Invariance: This axis assesses the stability of representations under perturbations or distribution shifts. Invariance is quantitatively measured by the cosine similarity between representations of transformed data and their unmodified counterparts.
• Disentanglement: Disentanglement evaluates the independence of latent dimensions concerning distinct FVs, providing insights into the modularity and decomposability of representations.

Experimental Evaluation

The research employs models from image and speech domains, testing their representations against specified parametric transformations related to color and auditory features, including hue, speech rate, and pitch. Results demonstrate that models with comparable downstream efficacy, such as SimCLR and DINO, exhibit substantial differences when analyzed across the proposed axes, suggesting varied internal mechanisms. The evaluation illustrates that standardized architectural designs alone do not suffice for capturing these nuanced representational behaviors.

Implications and Future Work

The implications of this framework are significant. By dissecting representation properties like equivariance and disentanglement, researchers can better understand the latent mechanisms influencing model performance, offering new directions for model selector practices in applications like retrieval, generation, and cross-domain adaptability. Furthermore, the findings inspire exploration into the structural properties driving generalization beyond simple task adequacy.

Future work should extend this framework to include higher-order transformations and apply the evaluation method to a broader range of domains and datasets. As this is a preliminary demonstration using simplified transformations, continued research could provide more complex and comprehensive insights into representation dynamics.

In conclusion, the paper advocates for a fundamental shift in how researchers evaluate model representations. By addressing intrinsic structural qualities that impact model utility and potential, this framework marks a step towards more holistic representation analysis, poised to inform future advancements in AI model development.