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Found 8 papers, 8 papers with code
Efficient generative adversarial networks using linear additive-attention Transformers
Although the capacity of deep generative models for image generation, such as Diffusion Models (DMs) and Generative Adversarial Networks (GANs), has dramatically improved in recent years, much of their success can be attributed to computationally expensive architectures.
Ranked #1 on
Image Generation
on LSUN Bedroom 128 x 128
Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification
In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailers12k, a new manually-curated movie trailer dataset composed of 12, 000 videos labeled with 10 different genres and associated metadata.
Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach
However, for NSTEMI, the top markers in women are low troponin levels, high urea levels, and age over 80 years.
Lightweight Speaker Verification for Online Identification of New Speakers with Short Segments
In this work we propose a BLSTM-based model that reaches a level of performance comparable to the current state of the art when using short input audio segments, while requiring a considerably less amount of memory.
Audio and Speech Processing Sound
A few filters are enough: Convolutional Neural Network for P300 Detection
Over the past decade, convolutional neural networks (CNNs) have become the driving force of an ever-increasing set of applications, achieving state-of-the-art performance.
Topic Discovery in Massive Text Corpora Based on Min-Hashing
This paper describes an alternative approach to discover topics based on Min-Hashing, which can handle massive text corpora and large vocabularies using modest computer hardware and does not require to fix the number of topics in advance.
Contextualize, Show and Tell: A Neural Visual Storyteller
We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015).
Ranked #22 on
Visual Storytelling
on VIST
Sampled Weighted Min-Hashing for Large-Scale Topic Mining
We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to automatically mine topics from large-scale corpora.
