View publication

Large neural networks pretrained on web-scale corpora are central to modern machine learning. In this paradigm, the distribution of the large, heterogeneous pretraining data rarely matches that of the application domain. This work considers modifying the pretraining distribution in the case where one has a small sample of data reflecting the targeted test conditions. We propose an algorithm motivated by a recent formulation of this setting as an online, bilevel optimization problem. With scalability in mind, our algorithm prioritizes computing gradients at training points which are likely to most improve the loss on the targeted distribution. Empirically, we show that in some cases this approach is beneficial over existing strategies from the domain adaptation literature but may not succeed in other cases. We propose a simple test to evaluate when our approach can be expected to work well and point towards further research to address current limitations.

Related readings and updates.

Memory-Retaining Finetuning via Distillation

This paper was accepted at the Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML) Workshop at NeurIPS 2024. Large language models (LLMs) pretrained on large corpora of internet text possess much of the world's knowledge. Following pretraining, one often needs to conduct continued pretraining on certain capabilities, such as math and coding, or "posttraining" (a.k.a., alignment) techniques to make the models follow users'…
See paper details

Bridging the Domain Gap for Neural Models

Deep neural networks are a milestone technique in the advancement of modern machine perception systems. However, in spite of the exceptional learning capacity and improved generalizability, these neural models still suffer from poor transferability. This is the challenge of domain shift—a shift in the relationship between data collected across different domains (e.g., computer generated vs. captured by real cameras). Models trained on data collected in one domain generally have poor accuracy on other domains. In this article, we discuss a new domain adaptation process that takes advantage of task-specific decision boundaries and the Wasserstein metric to bridge the domain gap, allowing the effective transfer of knowledge from one domain to another. As an additional advantage, this process is completely unsupervised, i.e., there is no need for new domain data to have labels or annotations.

See highlight details