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## Model Description
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- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
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- **Repository:** https://github.com/IMirandaM/BiVLC
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- **Paper:**
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- **Point of Contact:** [Imanol Miranda](mailto:imanol.miranda@ehu.eus)
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### Model Summary
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CLIP_Detector is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been trained with the OpenCLIP framework using the CLIP ViT-B-32 model pre-trained by 'openai' as a basis. For binary classification, the encoders are kept frozen. A sigmoid neuron is added over the CLS embedding for the image encoder and over the EOT embedding for the text encoder (more details in the paper). The objective of the model is to classify text and images as natural or synthetic. Hyperparameters:
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## Model Description
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- **Homepage:** https://imirandam.github.io/BiVLC_project_page/
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- **Repository:** https://github.com/IMirandaM/BiVLC
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- **Paper:** https://arxiv.org/abs/2406.09952
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- **Point of Contact:** [Imanol Miranda](mailto:imanol.miranda@ehu.eus)
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### Model Summary
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CLIP_Detector is a model presented in the [BiVLC](https://github.com/IMirandaM/BiVLC) paper for experimentation. It has been trained with the OpenCLIP framework using the CLIP ViT-B-32 model pre-trained by 'openai' as a basis. For binary classification, the encoders are kept frozen. A sigmoid neuron is added over the CLS embedding for the image encoder and over the EOT embedding for the text encoder (more details in the paper). The objective of the model is to classify text and images as natural or synthetic. Hyperparameters:
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