Invariant Feature Coding using Tensor Product Representation
Authors: YUSUKE Mukuta, Tatsuya Harada
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of our method is demonstrated on several image datasets. ... In this section, the accuracy and invariance of the proposed method are evaluated using the pretrained features in Section 5.1. In Section 5.2, we evaluate the method on an end-to-end case. |
| Researcher Affiliation | Academia | Yusuke Mukuta EMAIL The University of Tokyo, RIKEN |
| Pseudocode | Yes | Algorithm 1 Calculation of Invariant PCA ... Algorithm 2 Calculation of invariant k-means |
| Open Source Code | No | The pretraining code was implemented using Pytorch (Paszke et al., 2019) with the group equivariant convolution layers implemented using Groupy (Cohen & Welling, 2016). ... The training code was implemented with Pytorch, Groupy and fast-MPN-COV libraries (Li et al., 2018). |
| Open Datasets | Yes | These methods were evaluated using the Flickr Material Dataset (FMD) (Sharan et al., 2013), describable texture datasets (DTD) (Cimpoi et al., 2014), UIUC material dataset (UIUC) (Liao et al., 2013), Caltech UCSD Birds (CUB) (Welinder et al., 2010)) and Stanford Cars (Cars) (Krause et al., 2013). |
| Dataset Splits | Yes | We used given training test splits for DTD, CUB, and Cars. We randomly split 10 times such that the sizes of the training and testing data would be the same for each category for FMD and UIUC. |
| Hardware Specification | Yes | As for computation time, using an Intel Xeon E5-2698v4 x2 20 Core, 2.2 GHz CPU it takes 13 seconds to extract the training features and 61 seconds to learn SVM to train BP on UIUC... when using 8 A100 GPUs it takes 0.17 seconds/batch to train i SQRT-COV (Resnet50)... |
| Software Dependencies | No | The pretraining code was implemented using Pytorch (Paszke et al., 2019) with the group equivariant convolution layers implemented using Groupy (Cohen & Welling, 2016). ... The linear SVM implemented in LIBLINEAR (Fan et al., 2008) was used to evaluate the average test accuracy. |
| Experiment Setup | Yes | All the models were learned, including the feature extractor, using a momentum grad with an initial learning rate of 0.1, momentum of 0.9, and weight decay rate of 1e-4 for 65 epochs with a batch size of 160. The learning rate was multiplied by 0.1 at 30, 45, and 60 epochs. |