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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-to-text
+datasets:
+- MS-COCO
+- Flickr30k
+tags:
+- Image Captioning
+---
+
+# CapDec - NoiseLevel: 0.001
+
+## Model Description
+
+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).
+
+Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding.
+
+In their words:
+*Specifically, we assume that the visual embedding corresponding to a text embedding 
+lies somewhere within a ball of small radius around the text embedding (see Fig. 1). 
+We would like all text embeddings in this ball to decode to the same caption,which should 
+also correspond to the visual content mapped to this ball. We implement this intuition by 
+adding zero-mean Gaussian noise of STD to the text embedding before decoding it.*
+
+The "Noise Level" of 0.001 is equivalent to the Noise Variance which is the square of the STD.
+
+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. 
+
+## Datasets
+The authors trained the model on MS-COCO and Flickr30k datasets.
+
+## Performance
+The authors don't explicitly report the performance for this NoiseLevel but it can be estimated from the following figure from the original paper:
+![](capdec_performance.png)