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  # CapDec - NoiseLevel: 0.015
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- This are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf).
 
 
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  Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding.
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  The reported metrics are results of a model with a Noise Variance of 0.016, which the authors unfortunately do not provide in their repository.
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  This model with a Noise Variance 0.015 is the closest available pre-trained model to their best model.
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  ## Performance
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  The authors don't explicitly report the performance for this NoiseLevel but it can be estimated from the following figure from the original paper:
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  # CapDec - NoiseLevel: 0.015
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+ ## Model Description
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+
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+ These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf).
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  Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding.
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  The reported metrics are results of a model with a Noise Variance of 0.016, which the authors unfortunately do not provide in their repository.
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  This model with a Noise Variance 0.015 is the closest available pre-trained model to their best model.
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+ ## Datasets
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+ The authors trained the model on MS-COCO and Flickr30k datasets.
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  ## Performance
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  The authors don't explicitly report the performance for this NoiseLevel but it can be estimated from the following figure from the original paper:
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+ ![](capdec_performance.png)