Inverse Problems Leveraging Pre-trained Contrastive Representations
Authors: Sriram Ravula, Georgios Smyrnis, Matt Jordan, Alexandros G. Dimakis
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on a subset of Image Net and observe that our method is robust to varying levels of distortion. Our method outperforms end-to-end baselines even with a fraction of the labeled data in a wide range of forward operators. 4 Experiments |
| Researcher Affiliation | Academia | Sriram Ravula The University of Texas at Austin Electrical and Computer Engineering sriram.ravula@utexas.edu Georgios Smyrnis The University of Texas at Austin Electrical and Computer Engineering gsmyrnis@utexas.edu Matt Jordan The University of Texas at Austin Computer Science mjordan@cs.utexas.edu Alexandros G. Dimakis The University of Texas at Austin Electrical and Computer Engineering dimakis@austin.utexas.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/Sriram-Ravula/Contrastive-Inversion. |
| Open Datasets | Yes | For all experiments, we perform contrastive training for the robust encoder using a 100-class subset of Image Net, which we refer to as Image Net-100, [36, 39] to reduce computational resources. |
| Dataset Splits | Yes | We evaluate the quality of the learned robust representations for classifying images from the validation set of Image Net-100, using the same distortions during training and inference. |
| Hardware Specification | No | The paper mentions 'computing resources from TACC' but does not provide specific details on hardware components like GPU or CPU models, or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions using 'Pytorch' but does not specify its version number or any other software dependencies with their specific versions. |
| Experiment Setup | Yes | The baseline is trained for 25 epochs with a batch size of 64. Our robust encoder is trained for 25 epochs with a batch size of 256, and the linear probe on top of it is trained for 10 epochs. |