Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix
Authors: Hanyu Peng, Mingming Sun, Ping Li
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first conduct experiments comparing our approach versus extant post-hoc correction methods on three data sets, including CIFAR-100-LT (Cao et al., 2019), Image Net-LT (Liu et al., 2019), and i Naturalist (Horn et al., 2018) with varying backbones. Finally, we empirically make a comparison of our algorithm with alternative cutting-edge long-tailed recognition methods. |
| Researcher Affiliation | Industry | Hanyu Peng, Mingming Sun, Ping, Li Cognitive Computing Lab Baidu Research No.10 Xibeiwang East Road, Beijing 100193, China 10900 NE 8th St. Bellevue, Washington 98004, USA {penghanyu,sunmingming01,liping11}@baidu.com |
| Pseudocode | Yes | Algorithm 1: Solve OT-related algorithm efficiently in the post-hoc correction via Sinkhorn Algorithm. |
| Open Source Code | No | The paper mentions implementing experiments in Paddle Paddle but does not provide a statement about releasing its own source code or a link to it. |
| Open Datasets | Yes | We take experiments on three data sets including CIFAR-100-LT, Image Net LT, and i Naturalist. We build the imbalanced version of CIFAR-100 by downsampling samples per class following the profile in Liu et al. (2019); Kang et al. (2020) with imbalanced ratios 10, 50, and 100. |
| Dataset Splits | Yes | Having a collection of training samples {(xs n, ys n)}Ns n=1, validation samples {(xv n, yv n)}Nv n=1 and test samples {(xt n, yn)t}Nt n=1 for classification with K labels and input x Rd |
| Hardware Specification | Yes | Except for OTLM, which was run on an NVidia card (V100), the results come from a 28-core machine (2.20 Ghz Xeon). |
| Software Dependencies | No | The paper states, 'All our experiments are implemented in the Paddle Paddle deep learning platform,' but it does not specify version numbers for Paddle Paddle or any other software dependencies. |
| Experiment Setup | Yes | The specific implementation details for each data set under the different methods are described below. [...] We apply SGD with batch size 256 and weight decay 0.0005 to train a Res Net-32 (He et al., 2016) model for 200 epochs, we employ the linear warm-up learning rate schedule for the first five epochs. We also set the base learning rate to 0.2 and reduce it at epoch 120 and 160 by a factor of 100. |