Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch.

Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model.

This article explains more about an image classifier categorizing chest X-Ray images as Pneumonia or Normal. For this was used a dataset was found in Kaggle, using this link here. The code can be found on my GitHub. A pneumonia chest X-ray show abnormal opacification in one or both lungs, and a normal chest X-ray depicts clear lungs without any areas of abnormal opacification in the image. Each image in the dataset was graded by two expert physicians, as explained in Kaggle. The strategy used to work with this dataset (which has 1GB), was putting it in Google Drive and using Google Collab with TensorFlow and Keras.

Mixed reality and AI can help make surgeries safer by assisting surgeons during the process. From providing 3D imaging to handling instruments, AI is a vital part of the operating room. Here, we discuss what mixed reality means and how AI is taking surgeries to the next level. Artificial intelligence, machine learning, and computer vision are becoming an essential part of the healthcare industry. AI is helping doctors, nurses, and the hospital administration streamline patients' records, accurately diagnose the medical condition, and provide better treatment.

I thought I would take a moment to play with Watson Speech to Text and a utility that was released a few months ago. So the purpose of asking about a puppy is that I have a sample conversation system that is about buying a dog. Learn how to use Watson Speech to Text API to increase your accuracy. We've included links S2T utilities download links and sample .wav I thought I would take a moment to play with Watson Speech to Text and a utility that was released a few months ago.

June 23, 2017 Written by: Simon O'Doherty Key Points: – Learn how to use Watson Speech to Text utilities to increase your accuracy – We've included links so you can download S2T utilities – Sample .wav I thought I would take a moment to play with Watson Speech to Text and a utility that was released a few months ago. The Speech to Text Utils allows you to train S2T using your existing conversational system. To give a quick demo, I got my son to ask about buying a puppy. Of course the recording is crystal clear, which is why such a good result.

How to increase accuracy of results got using Tensorflow's retraining model? I formed my data set, ran the Tensorflow retraining code, trained the model and got my result. How do I increase it's accuracy further? Do I just keep giving it more training data?