Energy-based Automated Model Evaluation
Ru Peng, Heming Zou, Haobo Wang, Yawen Zeng, Zenan Huang, Junbo Zhao
TL;DR
This paper introduces Meta-Distribution Energy (MDE), a novel measure that enhances automated model evaluation by improving efficiency and effectiveness without relying on ground-truth labels, applicable across various modalities and learning scenarios.
Contribution
The paper proposes MDE, a new energy-based measure that improves AutoEval frameworks' performance and scalability, addressing overconfidence and computational issues in label-free model evaluation.
Findings
MDE outperforms prior approaches in diverse experiments.
MDE is effective across multiple modalities and architectures.
MDE seamlessly integrates with large-scale models and noisy data.
Abstract
The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. The Automated Model Evaluation (AutoEval) shows an alternative to this traditional workflow, by forming a proximal prediction pipeline of the testing performance without the presence of ground-truth labels. Despite its recent successes, the AutoEval frameworks still suffer from an overconfidence issue, substantial storage and computational cost. In that regard, we propose a novel measure -- Meta-Distribution Energy (MDE) -- that allows the AutoEval framework to be both more efficient and effective. The core of the MDE is to establish a meta-distribution statistic, on the information (energy) associated with individual samples, then offer a smoother representation enabled by energy-based learning. We further…
Peer Reviews
Decision·ICLR 2024 poster
This paper introduces an energy-based automatic evaluation framework designed to enhance efficiency and mitigate overconfidence in existing methodologies. The proposed approach indicates better prediction on unseen test data over the other measurement methods on dataset in different modalities and with different classification models.
1. It is recommended to use a different symbol for the normalization term E(x) in Eq(2) to avoid confusion, like Z(x). 2. In Eq(5) and Eq(6), the font of matchcal is usually used for a single letter. 3. I cannot see a clear relationship between the proposed method and the energy-based model except that "energy" specifies the logits from the classification model. 4. The performance of the proposed framework relies on the regression between MDE on the synthesized dataset and its accuracy. However
The authors have performed a wide range of analysis experiments.
1. The problem addressed - Authors motivate some new problem called Automated model evaluation. The definition is not very clear as they define it in many different ways. - I am not sure about the relevance of the problem. Or to phrase it better - I don't know much about this problem. 2. Proposed method - The proposed method just use energy based model equation to create a simple function of the energy expressed in terms of logits. - One concern here is that it might be very similar to some
+ Algorithm 1 clearly shows the proposed method. The whole pipeline is well-introduced + The experiment includes MNLI, a natural language inference task.
- *1. Overstated claims*: The paper asserts that "the AutoEval frameworks still suffer from an overconfidence issue" without providing clear, empirical examples. Additionally, the statement regarding "substantial storage" lacks a comparison with existing methods such as DoC and ATC, leaving the reader unconvinced of any real advantage of the proposed method. Furthermore, the claim that the proposed MDE method is superior in terms of "computational cost" is not substantiated, especially consideri
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Taxonomy
TopicsMachine Learning and Data Classification · Adversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI)