Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity.

OpenAI's management chaos in November could have long-lasting effects on its business as some of the company's customers say it was a wake-up call about the risks of being too reliant on one company's tech. Executives at companies that use OpenAI's software say they are increasingly looking to also use others' technology to protect themselves from the risks of problems at any one. OpenAI's competitors are using the opportunity to sign up wary customers.

I've been watching this trade pop up all over my newsfeed and I am so intrigued. I have a background in computer science and programming and I know enough to be dangerous, but this seems like it would take some serious skill. So what is the deal with automated trading bots? How can a bot make 1% daily profit? This post will answer all of your questions about automating cryptocurrency trading with artificial intelligence (AI) bots.

ACCOUNTANTS have been slow to adopt technology in recent years, although sophisticated software, tools, and solutions are now on the verge of reinventing the profession entirely. If accountants want to keep their edge, they need to learn to use technology -- at least to automate simple tasks and put them back in the driver's seat when it comes to managing the organization's financial strategy. RPA or robotic process automation is one technology they can quickly benefit from. It's easy to implement, affordable, and very effective. The technology could easily make repetitive, monotonous tasks redundant and free them up to get involved with data interpretation and management, allowing them to investigate errors and anomalies in data which requires their time, energy, and attention and has the potential to save the business money.

When we are investing in the market, we are all looking for an'edge'. As the markets are a zero-sum game, an edge is an unfair advantage that we believe will land us on the right side of a decision. This edge (whether real or perceived) can be your analysis of stocks, trusting the right expert, some intuition, technical analysis or using technology. The edge helps you be right a little more than 50 percent of the time, like a loaded coin in a coin toss that shows heads 51 out of 100 times. The only constant thing is that the edge is dynamic.

The world of work is witnessing tidal changes in business transformation made possible by the revolution in technology. Most notable is Artificial Intelligence (AI) which can be used to create new paradigms of collaboration and creativity. Here's how it can be a positive impact on an important and often overlooked segment of your employee population- your managers. Manager engagement begins by ensuring that they feel empowered to lead, make independent decisions and shoulder responsibilities. And if employee engagement is considered an art, then AI can bring the brushstrokes to your canvas.