无需元数据或训练数据，用模型权重直接搜能识别‘狗’的分类模型。
Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights
- 通过固定输入探针观测模型输出，生成每个逻辑值的特征描述。
- 在真实场景和细粒度搜索中均实现高精度，支持零样本文本检索。
- 结合协同过滤降低3倍编码成本，可扩展至全规模模型库。
随着公开可用模型数量增加，多数用户需求可能已有预训练模型。然而现有模型搜索方法仍停留在文档的文本检索，无法有效定位相关模型。本文提出ProbeLog，一种无需访问模型元数据或训练数据即可检索能识别目标概念（如“狗”）的分类模型的方法。与以往探测方法不同，ProbeLog通过观察模型在一组固定输入（探针）上的响应，为每个输出维度（logit）计算特征描述。该方法支持基于logit的检索（“找更多类似此logit的输出”）和零样本文本检索（“找所有对应‘狗’的logit”）。由于基于探测的表征需要多次昂贵的前向传播，我们设计了一种基于协同过滤的方法，将编码仓库的成本降低3倍。实验证明，ProbeLog在真实世界和细粒度搜索任务中均具备高检索准确率，且可扩展至完整规模的模型库。
原文摘要 · Abstract (English)
With the increasing numbers of publicly available models, there are probably pretrained, online models for most tasks users require. However, current model search methods are rudimentary, essentially a text-based search in the documentation, thus users cannot find the relevant models. This paper presents ProbeLog, a method for retrieving classification models that can recognize a target concept, such as "Dog", without access to model metadata or training data. Differently from previous probing methods, ProbeLog computes a descriptor for each output dimension (logit) of each model, by observing its responses on a fixed set of inputs (probes). Our method supports both logit-based retrieval ("find more logits like this") and zero-shot, text-based retrieval ("find all logits corresponding to dogs"). As probing-based representations require multiple costly feedforward passes through the model, we develop a method, based on collaborative filtering, that reduces the cost of encoding repositories by 3x. We demonstrate that ProbeLog achieves high retrieval accuracy, both in real-world and fine-grained search tasks and is scalable to full-size repositories.
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