Controlling Directions Orthogonal to a Classifier
Authors: Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we present three use cases where controlling orthogonal variation is important: style transfer, domain adaptation, and fairness. Empirically, we present three use cases where controlling orthogonal variation is important: style transfer, domain adaptation, and fairness. [...] 4.1 EXPERIMENTS [...] 5.1 EXPERIMENTS [...] 6.1 EXPERIMENTS |
| Researcher Affiliation | Academia | Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology {ylxu, haohe, tianxiao}@mit.edu; tommi@csail.mit.edu |
| Pseudocode | Yes | Algorithm 1 Classifier Orthogonalization |
| Open Source Code | Yes | The code is available at https://github.com/Newbeeer/orthogonal_classifier. |
| Open Datasets | Yes | CMNIST: We construct C(olors)MNIST dataset based on MNIST digits (Le Cun & Cortes, 2005). [...] Celeb A-GH: We construct the Celeb A-G(ender)H(air) dataset based on the gender and hair color attributes in Celeb A (Liu et al., 2015). [...] UCI Adult dataset [...] UCI German credit dataset |
| Dataset Splits | No | For all datasets, we use 0.8/0.2 proportions to split the train/test set. The paper specifies train/test splits but does not explicitly mention validation splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and other general software components, but it does not provide specific version numbers for libraries or frameworks like Python, PyTorch, or TensorFlow, which are necessary for reproducibility. |
| Experiment Setup | Yes | We adopt Adam with learning rate 2e-4 as the optimizer and batch size 128/32 for CMNIST/Celeb A. [...] We use the Adam with learning rate 1e-3 as the optimizer, and a batch size of 64. |