Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Authors: Floris Holstege, Bram Wouters, Noud Van Giersbergen, Cees Diks
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By evaluating the algorithm on benchmark datasets from computer vision (Waterbirds, Celeb A) and natural language processing (Multi NLI), we show it outperforms existing concept-removal methods in terms of identifying the main-task and spurious concepts, while removing only the latter. |
| Researcher Affiliation | Academia | 1University of Amsterdam, Department of Quantitative Economics 2Tinbergen Institute. Correspondence to: Floris Holstege <f.g.holstege@uva.nl>. |
| Pseudocode | Yes | Algorithm 1 JSE algorithm to estimate orthonormal bases for Zsp and Zmt. The conditions in the if-statements are discussed in Section 3.3. Input: a sample {ymt,k, ysp,k, zk}n k=1 consisting of two binary labels and a vector zk Rd. Initialize embedding matrix Z = (z1 z2 zn) . Initialize Z sp Z. for i = 1, ..., d do Zremain Z sp for j = 1, ..., d do Estimate ˆwsp, ˆwmt with Equation 2 (use Zremain). |
| Open Source Code | Yes | Our code with an implementation of JSE is publicly available.* *https://github.com/fholstege/JSE |
| Open Datasets | Yes | Waterbirds: this dataset from Sagawa et al. (2020b) is a combination of the Places dataset (Zhou et al., 2016) and the CUB dataset (Welinder et al., 2010)... Celeb A: this dataset contains images of celebrity faces (Liu et al., 2015)... Multi NLI: the Multi NLI dataset (Williams et al., 2018)... |
| Dataset Splits | Yes | For a given dataset size (e.g. n =2,000) the data is split into an 80% training and 20% validation set, and a test set of the same size is kept apart for evaluation. |
| Hardware Specification | No | The paper specifies the neural network architectures (Res Net50, BERT) and software libraries (torchvision, transformers) used, but does not provide any concrete details about the specific hardware (e.g., GPU models, CPU types) on which experiments were run. |
| Software Dependencies | No | The paper mentions 'torchvision package', 'transformers package', 'Pytorch', and 'Adam optimizer' but does not specify exact version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | For waterbirds, this means using a learning rate of 10 3, a weight decay of 10 3, a batch size of 32, and for 100 epochs without early stopping. For Celeb A, this means using a learning rate of 10 3, a weight decay of 10 4, a batch size of 128, and for 50 epochs without early stopping. |