Logit Mixing Training for More Reliable and Accurate Prediction
Authors: Duhyeon Bang, Kyungjune Baek, Jiwoo Kim, Yunho Jeon, Jin-Hwa Kim, Jiwon Kim, Jongwuk Lee, Hyunjung Shim
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results on the imageand language-based tasks demonstrate that Logit Mix achieves state-of-the-art performance among recent data augmentation techniques regarding calibration error and prediction accuracy. |
| Researcher Affiliation | Collaboration | Duhyeon Bang1 , Kyungjune Baek2 , Jiwoo Kim3 , Yunho Jeon4 , Jin-Hwa Kim5 , Jiwon Kim1 , Jongwuk Lee3 and Hyunjung Shim6 1SK T-Brain 2School of Integrated Technology, Yonsei University 3Department of Software, Sungkyunkwan University 4MOFL 5NAVER AI Lab 6 Kim Jaechul Graduate School of AI, KAIST |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Methods are described in text and mathematical formulas. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link or an explicit code release statement for the methodology described. |
| Open Datasets | Yes | The image datasets include CIFAR100 [Krizhevsky and Hinton, 2009] (32 32 RGB images in 100 classes), Tiny Image Net (64 64 RGB images in 100 classes) and ILSVRC2015 [Russakovsky et al., 2015] (256 256 RGB images in 1000 classes). Additionally, the General Language Understanding Evaluation (GLUE) benchmark [Wang et al., 2018]. |
| Dataset Splits | Yes | The image datasets include CIFAR100 [Krizhevsky and Hinton, 2009] (32 32 RGB images in 100 classes), Tiny Image Net (64 64 RGB images in 100 classes) and ILSVRC2015 [Russakovsky et al., 2015] (256 256 RGB images in 1000 classes). Additionally, the General Language Understanding Evaluation (GLUE) benchmark [Wang et al., 2018]. |
| Hardware Specification | Yes | To train the models on all the datasets except for ILSVRC2015, we use a single Titan XP GPU with 12 GB memory. For ILSVRC2015, we utilize four V100 GPU. |
| Software Dependencies | No | The paper mentions using "SGD optimization" and "BERT" but does not provide specific version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | When finetuning BERTBASE (or BERTLARGE), the batch size is 8, the learning rate is 2e 5, the max sequence length is 128, and the number of the training epochs is 3 for all eight tasks. We use a beta distribution with α = 3.0 for λ. |