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Yinong Wang

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TIST Journal 2026 Journal Article

Microscale-Searching Optimization for Transfer Learning-Based Filter Fine-Tuning

  • Le Feng
  • Fujian Feng
  • Li Xiao
  • Mian Tan
  • Han Huang
  • Yinong Wang

Fine-tuning has emerged as a popular technique in the field of transfer learning, demonstrating remarkable achievements in various data-scarce tasks. The performance of fine-tuning in deep convolutional neural networks depends on the selection of which parameters to fine-tune and freeze. However, it is difficult to determine which parameters in the pre-trained model need to be fine-tuned for a new task. This article proposes a filter-level discrete optimization model to identify the filter subset for fine-tuning, a core step of filter selection coding optimization. Due to the huge search space of the filter fine-tuning problem, we propose a filter interactivity decomposition strategy to find a valid search subspace (a smaller search subspace containing the optimal solution) by dividing the entire filter fine-tuning problem into multiple suboptimization problems. Based on the decomposition strategy, we design a microscale-searching transfer optimization algorithm, which solves each subproblem by searching the valid search subspace instead of the original search space of the filter fine-tuning problem. To verify the validity of the proposed algorithm, extensive experiments are conducted on seven publicly available image classification datasets: Stanford Dogs, MIT Indoors, Caltech 256-30, Caltech 256-60, Aircraft, UCF-101, and Omniglot. Experimental results show that the proposed method significantly improves the fine-tuning accuracy while effectively reducing the filter fine-tuning problem scale. Moreover, the proposed algorithm outperforms the state-of-the-art fine-tuning methods on the fine-tuning problem for transfer learning.

Details DOI

NeurIPS Conference 2024 Conference Paper

Visual Data Diagnosis and Debiasing with Concept Graphs

  • Rwiddhi Chakraborty
  • Yinong Wang
  • Jialu Gao
  • Runkai Zheng
  • Cheng Zhang
  • Fernando De la Torre

The widespread success of deep learning models today is owed to the curation of extensive datasets significant in size and complexity. However, such models frequently pick up inherent biases in the data during the training process, leading to unreliable predictions. Diagnosing and debiasing datasets is thus a necessity to ensure reliable model performance. In this paper, we present ConBias, a novel framework for diagnosing and mitigating Concept co-occurrence Biases in visual datasets. ConBias represents visual datasets as knowledge graphs of concepts, enabling meticulous analysis of spurious concept co-occurrences to uncover concept imbalances across the whole dataset. Moreover, we show that by employing a novel clique-based concept balancing strategy, we can mitigate these imbalances, leading to enhanced performance on downstream tasks. Extensive experiments show that data augmentation based on a balanced concept distribution augmented by ConBias improves generalization performance across multiple datasets compared to state-of-the-art methods.

PDF Details DOI
v2026.09.13