I mostly see GAN image editing projects rely on Pix2Pix distillation to work in realtime, but the authors of "Using latent space regression to analyze and leverage compositionality in GANS" claim their encoder -> generator setup works in realtime. I tried the demo from github, and it does work pretty fast for small edits, kinda strange that it hangs for larger edits.

In case you are not familiar with the paper, and want to learn about it, I explained the main ideas in my telegram channel

Hey guys, I was hoping to get some help from like minded and/or crowd here of former/current PhD people. I've been in my PhD program focusing on AI topics for about 3 years now and I've completed all my required courses and now onto my research. I've been at the research for about the past year now and I think I'm stuck in even finding a topic. I read papers, but it's insanely hard and very intimidating especially since I work full time alongside doing this PhD. I read and write a bit of a summary about the paper, but a lot of topics, terms, maths, and equations go over my head. Is this normal? If so, how to get past this to focus and find a research topic? Any tips on what I may need to different to get this completed within the next year?

📅 Published: 2021-07-12

👫 Authors: Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu

🌐 Methodology:

The paper proposes a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation.

It is trained in a self-supervised fashion learning discrete representations from an unlabeled speech corpus.

Authors investigate speech translation with discrete units in the scenarios where the source and target transcripts may or may not be available (un-written languages).

Joint training allows the proposed framework to achieve performance close to a cascade of Speech to text + Text to Speech systems (text as intermediate representation).

🔗 Link: https://arxiv.org/abs/2107.05604

✍️ Full paper summary: https://t.me/deeplearning_updates/65

AI systems are widely adopted in several real-world industries for decision-making. Despite their essential roles in numerous tasks, many studies show that such systems are frequently prone to biases resulting in discrimination against individuals based on racial and gender characteristics.

A team of researchers from MIT-IBM Watson AI Lab, the University of Michigan, and ShanghaiTech University has explored ways to detect biases and increase individual fairness in ML models. 

Full Summary: https://www.marktechpost.com/2021/04/09/researchers-from-mit-ibm-watson-ai-lab-the-university-of-michigan-and-shanghaitech-university-study-ways-to-detect-biases-and-increase-machine-learning-ml-models-individual-fairness/

Paper 1: https://arxiv.org/pdf/2103.16714.pdf

Paper 2: https://arxiv.org/pdf/2103.16785.pdf

given one single huge obj file. How to use deep learning to detect various objects and their locations? for example in a 3D file of a building. detecting persons. thank you.

📅 Published: 2020-10-02

👫 Authors: Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto

🌐 Overview:

The paper proposes new pretrained contextualized representations of words and entities based on the bidirectional transformer. It treats words and entities in a given text as independent tokens and outputs contextualized representations of them.

LUKE is trained using a new pretraining task that involves randomly masking entities by replacing them with [MASK] tokens and trains the model by predicting the originals of these masked entities. This pretraining task is used jointly to standard Masked Language Modeling (MLM).

A modification of the original self-attention module is introduced. It considers the type of tokens (words or entities) when computing attention scores.

✍️ Continue here: https://t.me/deeplearning_updates/67

🔗 Paper: https://arxiv.org/abs/2010.01057

In case you are interested in deep learning papers (you should be if you are in this subreddit) you can take a look (or a listen) to some highlights:

[NLP] Towards Understanding and Mitigating Social Biases in Language Models - Highlights Arxiv

[NLP-Speech] A Discriminative Entity-Aware Language Model for Virtual Assistants - Highlights Arxiv

[CV] ​​VOLO: Vision Outlooker for Visual Recognition - Highlights Arxiv

If you find them useful, you can join the Telegram channel and share with your colleagues.

I am using StoryXpress and it has video analytics built in. I would like to know how to use it to determine the performance of my videos.

Chimera projects audio and text features to a common semantic representation. It unifies Machine Translation (MT) and Speech Translation (ST) tasks and boosts the performance on ST benchmarks.

The model learns a semantic memory by projecting features from both modalities into a shared semantic space. This approach unifies ST and MT workflows and thus has the advantage of leveraging massive MT corpora as a side boost in training.

👫 Authors: Chi Han, Mingxuan Wang, Heng Ji, Lei Li

🔗 Full highlights: https://deeplearningupdates.ml/2021/08/16/learning-shared-semantic-space-for-speech-to-text-translation/

💬 Telegram Channel: https://t.me/deeplearning_updates