Out-Of-Domain Unlabeled Data Improves Generalization
Authors: seyed amir hossein saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl Motahari, Babak Khalaj
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd... We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Department of Computer Engineering, Sharif Center for Information Systems and Data Science, Sharif Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran |
| Pseudocode | Yes | Algorithm 1 Finding the adversarial perturbed input for original input data based on gradient ascent |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository nor explicitly states that the code for their method is being released. |
| Open Datasets | Yes | NCT-CRC-HE-100K consists of 100,000 histopathology images of colon tissue (Katherm et al., 2018). |
| Dataset Splits | No | Finally, we select a combination of hyper-parameters that achieved the highest accuracy on a validation dataset, and we report the accuracy of our model, using these hyper-parameters, on the test samples. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, or memory) used to run its experiments. |
| Software Dependencies | Yes | The codes are written using the Python programming language and the Pytorch 2.0 machine learning framework. |
| Experiment Setup | Yes | A random search process has been performed to find the optimum γ, γ , λ, and weight-decay. Finally, we select a combination of hyper-parameters that achieved the highest accuracy on a validation dataset, and we report the accuracy of our model, using these hyper-parameters, on the test samples. |