FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning
Authors: Kun Song, Huimin Ma, Bochao Zou, Huishuai Zhang, Weiran Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results validate the efficacy of our approach for both ID and OOD tasks. |
| Researcher Affiliation | Collaboration | 1SCCE, University of Science and Technology Beijing 2Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University 3Microsoft Research Asia |
| Pseudocode | No | The paper describes the proposed method in textual format but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code could be found in https:// github.com/skingorz/FD-Align. |
| Open Datasets | Yes | For the OOD setting, we evaluate our method on two different datasets. On the one hand, we train the model on Image Net [27] following the few-shot task in CLIP and test the performance on two OOD variants of Image Net [27]: Image Net V2 [28] and Image Net-Sketch [29] with the same 1000 classes, On the other hand, we follow the traditional few-shot learning strategy and fine-tune the model on the train split of mini Image Net and evaluate the model on Meta-dataset [30], BSCDFSL benchmark [31] and Domain Net [32], for a total of 19 datasets. |
| Dataset Splits | Yes | Figure 9 depicts the evolution of model accuracy and loss on the validation set throughout the fully fine-tuning and FD-Align processes. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions using 'open source Vi T-B/32 as the backbone of the CLIP' and 'Open AI Image Net prompt templates' but does not specify software versions for any libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages. |
| Experiment Setup | Yes | Ltotal = α Lclass + β Lspurious, where we set α to 1 and β to 20 in this paper. ... we set n to 60, k to 20 in the spurious prototype correction stage. We employ the Stochastic Gradient Descent (SGD) optimizer for model fine-tuning, conducting the process over 60 epochs. |