Autoencoders are a class of artificial neural networks which have gained a lot of attention in the recent past. Using the encoder block of an autoencoder the input image can be compressed into a meaningful representation. Then a decoder is employed to reconstruct the compressed representation back to a version which looks like the input image. It has plenty of applications in the field of data compression and denoising. Another version of Autoencoders (AE) exist, called Variational AE (VAE) which acts as a generative model like GAN. Recently, an optimizer was introduced which is known as lookahead optimizer which significantly enhances the performances of Adam as well as SGD. In this paper, we implement Convolutional Autoencoders (CAE) and Convolutional Variational Autoencoders (CVAE) with lookahead optimizer (with Adam) and compare them with the Adam (only) optimizer counterparts. For this purpose, we have used a movie dataset comprising of natural images for the former case and CIFAR100 for the latter case. We show that lookahead optimizer (with Adam) improves the performance of CAEs for reconstruction of natural images.

For the longest time, artificial intelligence (AI) was nothing more than exciting material for science fiction novels and movies. In 1982, Harrison Ford was hunting down replicants in'Blade Runners'; in 2001, Steven Spielberg wove an intricate tale of a relationship between human and machine in'A. Technologies that emulate or surpass human skills and characteristics are fascinating – not only on the big screen. For decades, scientists have dedicated time and money to the topic of artificial intelligence. The workshop'Dartmouth Summer Research Project on Artificial Intelligence', held in 1956 at Dartmouth College in New Hampshire, USA, is seen as the starting point of AI as academic subject.

I have a large dataset of images. I have a few examples(1-100) of some small objects X that occasionally appear in the images. I want to find all images that contain object X. This object might be anywhere between 30% to 5% of the size of the image.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

We've seen no shortage of scandals when it comes to AI. In 2016, Microsoft Tay, an AI bot built to learn in real time from social media content turned into a misogynist, racist troll within 24 hours of launch. A ProPublica report claimed that an algorithm -- built by a private contractor -- was more likely to rate black parole candidates as higher risk. A landmark U.S. government study reported that more than 200 facial recognition algorithms -- comprising a majority in the industry -- had a harder time distinguishing non-white faces. The bias in our human-built AI likely owes something to the lack of diversity in the humans who built them.

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Making a slight mistake during live music performance can easily be spotted by an astute listener, even if the performance is an improvisation or an unfamiliar piece. An example might be a highly dissonant chord played by mistake in a classical-era sonata, or a sudden off-key note in a recurring motif. The problem of identifying and correcting such errors can be approached with artificial intelligence – if a trained human can easily do it, maybe a computer can be trained to spot the errors quickly and just as accurately. The ability to identify and auto-correct errors in real-time would be not only extremely useful to performing musicians, but also a valuable asset for producers, allowing much fewer overdubs and re-recording of takes due to small imperfections. This paper examines state-of-the-art solutions to related problems and explores novel solutions for music error detection and correction, focusing on their real-time applicability.

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to your applications. Today, we're excited to launch Japanese, Korean, and Brazilian Portuguese language support for Amazon Transcribe streaming. To deliver streaming transcriptions with low latency for these languages, we're also announcing availability of Amazon Transcribe streaming in the Asia Pacific (Seoul), Asia Pacific (Tokyo), and South America (São Paulo) Regions. Amazon Transcribe added support for Italian and German languages earlier in November 2020, and this launch continues to expand the service's streaming footprint. Now you can automatically generate live streaming transcriptions for a diverse set of use cases within your contact centers and media production workflows.

If you're worried about facial recognition firms or stalkers mining your online photos, a new tool called Anonymizer could help you escape their clutches. The app was created by Generated Media, a startup that provides AI-generated pictures to customers ranging from video game developers creating new characters to journalists protecting the identities of sources. The company says it built Anonymizer as "a useful way to showcase the utility of synthetic media." The system was trained on tens of thousands of photos taken in the Generated Media studio. The pictures are fed to generative adversarial networks (GANs), which create new images by pitting two neural networks against each other: a generator that creates new samples and a discriminator that examines whether they look real. The process creates a feedback loop that eventually produces lifelike profile photos.

The robot is modeled after a figure in "Mobile Suit Gundam," a Japanese cartoon first launched in the late 1970s about enormous battle robots piloted by humans. The series spawned multiple spin-offs and toys and gained a worldwide following.