#Gif deep dreamer free#

The first question we wanted to answer for these dreaming processes is how the input representation evolves. This is a question we tried to answer PAIR-style, by providing explainability approaches to visually inspect those dreaming results.

#Gif deep dreamer free#

We used these tools to reason about our approaches with BERT, but if you run into similar problems, feel free to use them with any model.Īll tools presented within this are publicly available on GitHub. Therefore, we also make use of a temperature parameter, which controls the sparsity of this linear combination.īy slowly decreasing this temperature value, we can make the model first explore different linear combinations of tokens, before deciding on one token. This, however, still leaves the problem that we can end up with any linear combination of such tokens, including ones that are not close to real tokens in the embedding space. To assure that we do not end up with something crazy, it uses two mechanisms.įirst, it constrains this linear combination so that the linear weights sum up to one. It allows us to soften the requirement for discrete inputs, and instead use a linear combination of tokens as input to the model. To still be able to use Deep Dream, we have to utilize the so-called Gumbel-Softmax trick, which has already been employed in a paper by Poerner et. With Deep Dream changing the embeddings rather than input tokens, we can end up with embeddings that are nowhere close to any token. You might ask: 'Why can't we use these embeddings to dream to?' The answer is that there is often no mapping from unconstrained embedding vectors back to real tokens. In this embedding space, words with similar meanings are closer together than words with different meanings. Using these embeddings, words are converted into high-dimensional vectors of continuous numbers. Language models operate on embeddings of words. This way, we get an idea about which part of the network is looking for what kind of input. The ideas of feature visualization are borrowed from Deep Dream, where we can obtain inputs that excite the network by maximizing the activation of neurons, channels, or layers of the network. Whenever such breakthroughs in deep learning happen, people wonder how the network manages to achieve such impressive results, and what it actually learned.Ī common way of looking into neural networks is feature visualization. This novel model has brought a big change to language modeling as it outperformed all its predecessors on multiple different tasks. It can be used for multiple different tasks, such as sentiment analysis or next sentence prediction, and has recently been integrated into Google Search. A visual investigation of nightmares in Sesame Street by Alex Bäuerle ( and James Wexler ( of PAIR.īERT, a neural network published by Google in 2018, excels in natural language understanding.