Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Authors: Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment In this section, we initiate OLS-OFU with three popular SSL techniques and empirically evaluate how OLS-OFU improves the original OLS methods on both online label shift and online generalized label shift on various datasets and shift patterns. |
| Researcher Affiliation | Collaboration | Ruihan Wu UC San Diego ruw076@ucsd.edu Siddhartha Datta University of Oxford siddhartha.datta@cs.ox.ac.uk Yi Su Google Deep Mind yisumtv@google.com Dheeraj Baby UC Santa Barbara dheeraj@ucsb.edu Yu-Xiang Wang UC San Diego yuxiangw@ucsd.edu Kilian Q. Weinberger Cornell University kilian@cornell.edu |
| Pseudocode | Yes | Algorithm 1 Online label shift adaptation with online feature updates (OLS-OFU). |
| Open Source Code | Yes | Code is released at https://github.com/dattasiddhartha/online-feature-updates-olsofu |
| Open Datasets | Yes | For online label shift, we evaluate the efficacy of our algorithm on CIFAR-10 [29], STL10 [12], CINIC [13], and Euro SAT [25]. For online generalized label shift, the offline train and validation sets are the CIFAR-10 images. The test unlabeled batches are drawn from CIFAR-10C [26], a benchmark with the same objects as CIFAR-10 but with various types of corruption. |
| Dataset Splits | Yes | For each dataset, we split the original train set into the offline train (i.e., D0) and validation sets (i.e., D 0) following a ratio of 4 : 1. |
| Hardware Specification | No | The paper mentions evaluating methods with ResNet18 and discusses time costs, but does not specify the hardware (e.g., GPU model, CPU, memory) used for these experiments. |
| Software Dependencies | No | The paper refers to using PyTorch (implicitly for neural networks) and ResNet18, but does not specify version numbers for any software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We experiment with T = 1000 and batch size B = 10 at each time step, following Baby et al. [6]. The frequency parameter τ is fixed as 100 for most experiments unless we particularly mention it. The seed used to train our model is 4242, and we train an additional 4 models on seeds 4343, 4545, 4646, 4747. |