Unsupervised Learning under Latent Label Shift
Authors: Manley Roberts, Pranav Mani, Saurabh Garg, Zachary Lipton
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In semi-synthetic experiments, we adapt existing image classification benchmarks to the LLS setting, sampling without replacement to construct collections of label-shifted domains. and Table 1: Results on CIFAR-20. Each entry is produced with the averaged result of 3 different random seeds. |
| Researcher Affiliation | Academia | Manley Roberts Pranav Mani Saurabh Garg Zachary C. Lipton Carnegie Mellon University {manleyroberts,zlipton}@cmu.edu; {pmani, sgarg2}@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 DDFA Training and Algorithm 2 DDFA Prediction |
| Open Source Code | Yes | Code is available at https://github.com/acmi-lab/Latent-Label-Shift-DDFA. |
| Open Datasets | Yes | First we examine standard multiclass image datasets CIFAR-10, CIFAR-20 [43], and Image Net-50 [19] containing images from 10, 20, and 50 classes respectively. |
| Dataset Splits | Yes | Algorithm 1 DDFA Training: 1: Split into train set T and validation set V and The algorithm uses train and validation data consisting of pairs of images and domain indices. We train Res Net50 [35] (with added dropout) on images xi with domain indices di as the label, choose best iteration by valid loss, pass all training and validation data through pf, and cluster pushforward predictions pfpxiq into m ě k clusters with Faiss K-Means [42]. |
| Hardware Specification | Yes | We use one NVIDIA V100 GPU for all experiments. |
| Software Dependencies | No | The paper mentions several software components and libraries, such as 'Res Net50', 'Sim CLR', 'Mo Co', 'Faiss K-Means', and 'Sklearn implementation of standard NMF', but it does not specify any version numbers for these dependencies. |
| Experiment Setup | Yes | We train for 100 epochs with a learning rate of 1e-4, batch size of 256, and Adam optimizer. We use a learning rate of 1e-3, batch size of 256 for SCAN. |