A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Authors: Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive real-world face recognition (with domain shifts and simultaneous multi-domains inputs), image classification, and segmentation experiments, and observe that, with the proposed method, invariant representations and performance across domains are consistently achieved without compromising the performance of individual domain. |
| Researcher Affiliation | Academia | Purdue University1 Duke University2 |
| Pseudocode | No | The paper describes the method using text and diagrams but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | We adopt a challenging setting by using MNIST as the source domain, and SVHN as the target domain. [...] We adopt the NIR-VIS 2.0 [16], which consists of 17,580 NIR (near infrared) and VIS (visible light) face images of 725 subjects, and perform cross-domain face recognition. [...] We perform experiments on three public digits datasets: MNIST, USPS, and Street View House Numbers (SVHN). [...] Office-31 [27] is one of the most widely used datasets for visual domain adaptation. [...] We perform unsupervised adaptation from the GTA dataset [24] (images generated from video games) to the Cityscapes dataset [4] (real-world images). |
| Dataset Splits | Yes | We start the comparisons at 10% of the target domain labeled samples, and end at 0.5% where only 366 labeled samples are available for the target domain. |
| Hardware Specification | No | The paper mentions VGG-16 as a base network structure and discusses its parameters and FLOPs, but does not specify the hardware (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | No | The paper mentions that 'networks with DAFD are trained end-to-end with a summed loss for domains' and how atoms/coefficients are updated, but it does not provide specific hyperparameters like learning rates, batch sizes, optimizers, or number of epochs. |