KR-20260067113-A - HYBRID GENERATIVE MODEL APPARATUS AND METHOD FOR DETECTING OUT-OF-DISTRIBUTION USING THE SAME

Abstract

The present invention relates to a hybrid generative model device and a method for determining out-of-distribution using the same, comprising: a first input unit for inputting training data; a second input unit for inputting test data; a learning model unit for inputting the training data into a hybrid generative model to train it, and inputting and outputting the test data into the hybrid generative model; and an OOD determination unit for determining out-of-distribution (OOD) through the Wasserstein distance and mutual information of the training data and test data measured according to the output of the hybrid generative model, and the minimal description length of the test data. By including these components, it is possible to effectively determine distribution data and out-of-distribution data using a hybrid generative model, as well as effectively ensure the integrity of the image.

Inventors

• 최정렬
• 안지승
• 이기웅
• 김영욱

Assignees

• (주)아이옵스

Dates

Publication Date

20260512

Application Date

20241105

Claims (10)

1. A first input unit for inputting training data; A second input unit for inputting test data; A learning model unit that inputs the above training data into a hybrid generative model for training, and inputs and outputs the above test data into the hybrid generative model; and An OOD determination unit that determines Out-Of-Distribution (OOD) through the Wasserstein Distance and Mutual Information of the training data and test data measured according to the output of the hybrid generative model, and the Minimal Description Length of the test data; A hybrid generative model device including
2. In claim 1, The above training data is, Using In Distribution data, The above test data is, Includes Out-Of-Distribution data Hybrid generative model device.
3. In claim 2, The above hybrid generative model is, Includes a Normalizing Flow model Hybrid generative model device.
4. In claim 3, The above OOD discrimination unit is, Determining the above OOD by deriving a damage difficulty ranking using the above Wasserstein distance, mutual information, and minimum explanation length Hybrid generative model device.
5. In claim 4, The above OOD discrimination unit is, Deriving the above injury difficulty ranking based on changes in covariates and semantic changes for the above training data and test data Hybrid generative model device.
6. A step of inputting training data through the first input unit; A step of inputting the above training data into a hybrid generative model in the learning model section to train it; A step of inputting test data through the second input unit; A step of inputting and outputting the test data to the hybrid generative model in the above learning model unit; and A step of determining Out-Of-Distribution (OOD) through the Wasserstein Distance and Mutual Information of the training data and test data measured according to the output of the hybrid generative model in the OOD determination unit, and the Minimal Description Length of the test data; A method for determining out-of-distribution using a hybrid generative model device including
7. In claim 6, The above training data is, Using In Distribution data, The above test data is, Using Out-Of-Distribution data Method for determining out-of-distribution using a hybrid generative model device.
8. In claim 7, The above hybrid generative model is, Includes a Normalizing Flow model Method for determining out-of-distribution using a hybrid generative model device.
9. In claim 8, The step of determining the above OOD is, The above OOD discrimination unit derives a damage difficulty ranking using the above Wasserstein distance, mutual information, and minimum explanation length to identify the above OOD. Method for determining out-of-distribution using a hybrid generative model device.
10. In claim 9, The step of determining the above OOD is, The above OOD discrimination unit derives the above injury difficulty ranking based on changes in covariates and semantic changes for the above training data and test data. Method for determining out-of-distribution using a hybrid generative model device.

Description

Hybrid Generative Model Apparatus and Method for Detecting Out-of-Distribution Using the Same The present invention relates to a hybrid generative model device and a method for determining out-of-distribution using the same, which can effectively distinguish between distribution data and out-of-distribution data using a hybrid generative model and effectively ensure image integrity by determining out-of-distribution (OOD) through the Wasserstein distance and mutual information of training data and test data measured according to the output of the hybrid generative model and the minimum description length of the test data. As is well known, the field of image classification using deep learning has recently been actively researched. However, general image classification models output the closest class based on the assumption that the image belongs to a target distribution, and out-of-distribution (OOD) detection, which detects abnormal data outside the target distribution, is emerging as an important problem in deep learning image classification. OOD detection in such image classification is a technique that allows a model to determine whether an image to be classified belongs to a target distribution. Techniques are being proposed such as training the model with OOD detection as a task during the training process, or analyzing parameters calculated when an image is input into the model. As described above, ODD detection in image classification models is to determine that OOD data is input to the model when it does not correspond to the target distribution. To address this, the softmax score output from the model can be used to distinguish between In Distribution (ID) data and OOD data. For example, techniques such as input processing on images and techniques that consider OOD data from the training stage of the model have been proposed. Meanwhile, in image classifiers for autonomous vehicles, new input data that was not seen during training (e.g., wild animals passing on a highway) may be input, and to address this, ODD detection is essential. In broad classification systems that process images, such as in the field of deepfakes, anomaly detection, new object detection, and inductive and transfer learning tasks are emerging, and various techniques are being proposed. FIG. 1 is a block diagram of a hybrid generative model device according to one embodiment of the present invention, and FIGS. 2 to 8 are drawings for explaining the detailed configuration of a hybrid generative model device according to an embodiment of the present invention, and FIG. 9 is a flowchart illustrating the process of determining out-of-distribution using a hybrid generative model device according to another embodiment of the present invention. The advantages and features of the embodiments of the present invention, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. In describing the embodiments of the present invention, specific descriptions of known functions or configurations will be omitted if it is determined that such detailed descriptions could unnecessarily obscure the essence of the invention. Furthermore, the terms described below are defined in consideration of their functions in the embodiments of the present invention, and these definitions may vary depending on the intentions or practices of the user or operator. Therefore, such definitions should be based on the content throughout this specification. Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a block diagram of a hybrid generative model device according to one embodiment of the present invention, and FIGS. 2 to 8 are drawings for explaining the detailed configuration of a hybrid generative model device according to one embodiment of the present invention. Referring to FIGS. 1 to 8, a hybrid generative model device according to one embodiment of the present invention may include a first input unit (110), a second input unit (120), a learning model unit (130), an OOD discrimination unit (140), etc. The first input unit (110) is a component that inputs training data, and the training data can be, for example, In Distribution data. Training data using such attribution data (ID) can be input into the learning model unit (130) for training a hybrid generative model. The second input unit (120) is a component that inputs test data