OGEN Synthesizes Features for Unknown Classes Using Only Their Names and Regularizes the Decision Boundary Between Known and Unknown Classes, Achieving Significant Gains in Various Settings.
- Paper Title: Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
- Journal Name: International Conference on Learning Representations (ICLR) 2024
- Authors’ Names: Yuhang Zang, Hanlin Goh, Josh Susskind, Chen Huang
What Problem this Paper Solves?
Vision-language models, such as CLIP, can perform zero-shot recognition on a variety of visual domains and tasks. However, they mainly operate in a closed-set manner, meaning that they assume that the input image belongs to one of the predefined classes. This limits their ability to handle open-domain visual concepts that are not seen during pre-training.
Moreover, existing finetuning methods for vision-language models tend to overfit the known classes in the given dataset, resulting in degraded performance on unknown classes. Therefore, there is a need for a method that can improve the out-of-distribution (OOD) generalization of vision-language models, i.e., their ability to detect and identify novel classes that are not in the training data.
What Approach Does this Paper Utilize?
The paper proposes OGEN, a novel approach that leverages a class-conditional feature generator to synthesize OOD features using just the class name of any unknown class. For example, given the class name “panda”, OGEN can generate a feature vector that is representative of pandas, even if the model has never seen a panda image before.
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Such synthesized features provide useful knowledge about unknown classes and help regularize the decision boundary between in-distribution (ID) and OOD data when optimized jointly with the vision-language model. Furthermore, the paper introduces an adaptive self-distillation mechanism to regularize the feature generation model during joint optimization. This means that the feature generator can learn from its own previous states and avoid overfitting to the training data.
What Are the Impacts of this Approach on AI Research?
The paper demonstrates that OGEN can significantly improve the OOD generalization performance of vision-language models in different settings, such as within-dataset, cross-dataset, and cross-domain. OGEN outperforms existing finetuning methods, such as prompt learning and adaptor tuning, as well as baseline methods, such as softmax scaling and text generation. OGEN also shows robustness to different choices of class names and feature generation models.
The paper contributes to the AI research by addressing a major challenge and safety risk of vision-language models in the open domain, where novel visual concepts are abundant and unpredictable. The paper also provides insights into the role of feature generation and self-distillation in enhancing the generalization capabilities of vision-language models.
Discussion on Results
The paper presents extensive experiments to evaluate the effectiveness of OGEN on various datasets and tasks, such as ImageNet, EuroSAT, UCF101, and DTD. The paper also conducts ablation studies to analyze the impact of different components and hyperparameters of OGEN. The paper reports the following main results:
- OGEN achieves state-of-the-art OOD generalization performance on all datasets and tasks, surpassing existing finetuning methods and baseline methods by a large margin.
- OGEN improves both ID and OOD accuracies after finetuning, while other methods tend to sacrifice one for the other.
- OGEN is robust to different choices of class names and feature generation models, indicating that OGEN can generalize to any unknown class name and leverage any feature generator.
- OGEN benefits from the adaptive self-distillation mechanism, which prevents the feature generator from overfitting and improves the OOD generalization performance.
Summary of the Research
The paper proposes OGEN, a novel approach to improve the out-of-distribution generalization of vision-language models. OGEN leverages a class-conditional feature generator to synthesize features for unknown classes using only their names, and regularizes the decision boundary between known and unknown classes. OGEN also introduces an adaptive self-distillation mechanism to regularize the feature generation model during joint optimization.
The paper demonstrates that OGEN can significantly improve the OOD generalization performance of vision-language models in different settings, such as within-dataset, cross-dataset, and cross-domain. OGEN outperforms existing finetuning methods and baseline methods by a large margin. OGEN also shows robustness to different choices of class names and feature generation models. The paper contributes to the AI research by addressing a major challenge and safety risk of vision-language models in the open domain, where novel visual concepts are abundant and unpredictable.
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