Papers with image classification models
An Effective Label Noise Model for DNN Text Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train deep neural networks with label noise are limited to image classification models . label noise is important because of the large number of errors and errors in training datasets . |
| Approach: | They propose a non-linear processing layer that models label noise into a convolutional neural network (CNN) they add a noise model layer on top of their target model to account for label noise . |
| Outcome: | The proposed approach is robust to label noise and can learn better sentences . it is based on extensive experiments on text classification datasets . |
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Approach: | They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Outcome: | The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models. |