No abstract

No abstract

Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions"-noisy, potentially correlated binary functions intended to capture possible axes of distribution shift-to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3× more accurately compared to standard baselines.

In this paper we explore the viability of path tracing massive scenes using a "supercomputer" constructed on-the-fly from thousands of small, serverless cloud computing nodes. We present R2E2 (Really Elastic Ray Engine) a scene decomposition-based parallel renderer that rapidly acquires thousands of cloud CPU cores, loads scene geometry from a pre-built scene BVH into the aggregate memory of these nodes in parallel, and performs full path traced global illumination using an inter-node messaging service designed for communicating ray data. To balance ray tracing work across many nodes, R2E2 adopts a service-oriented design that statically replicates geometry and texture data from frequently traversed scene regions onto multiple nodes based on estimates of load, and dynamically assigns ray tracing work to lightly loaded nodes holding the required data. We port pbrt's ray-scene intersection components to the R2E2 architecture, and demonstrate that scenes with up to a terabyte of geometry and texture data (where as little as 1/250th of the scene can fit on any one node) can be path traced at 4K resolution, in tens of seconds using thousands of tiny serverless nodes on the AWS Lambda platform.

We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to the background category in the dataset (>95% of the dataset is background). We demonstrate that both standard fine-tuning approaches and stateof-the-art approaches for training on imbalanced datasets do not produce accurate deep models in the presence of this extreme imbalance. Our key observation is that the extreme imbalance due to the background category can be drastically reduced by leveraging visual knowledge from an existing pre-trained model. Specifically, the background category is "split" into smaller and more coherent pseudo-categories during training using a pre-trained model. We incorporate background splitting into an image classification model by adding an auxiliary loss that learns to mimic the predictions of the existing, pre-trained image classification model. Note that this process is automatic and requires no additional manual labels. The auxiliary loss regularizes the feature representation of the shared network trunk by requiring it to discriminate between previously homogeneous background instances and reduces overfitting to the small number of rare category positives. In addition, we also show that background splitting can be combined with other methods which modify the main classification loss to deal with background imbalance to further improve performance, indicating that background splitting is a complementary method to existing imbalanced learning techniques. We evaluate our method on a modified version of the iNaturalist dataset where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize ImageNet categories and selected iNaturalist categories, our approach yields performance that is 42.3 mAP points higher than a standard finetuning baseline when 99.98% of the data is background, and 8.3 mAP points higher than state-of-the-art baselines when 98.30% of the data is background.