Results 21 to 30 of about 1,645,972 (339)
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation [PDF]
Computer Vision and Pattern Recognition, 2020 We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the ...
Elad Richardson +6 more
semanticscholar +1 more source
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement [PDF]
IEEE International Conference on Computer Vision, 2021 Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it ...
Yuval Alaluf, Or Patashnik, D. Cohen-Or
semanticscholar +1 more source
ENCODE data at the ENCODE portal [PDF]
Nucleic Acids Research, 2015 The Encyclopedia of DNA Elements (ENCODE) Project is in its third phase of creating a comprehensive catalog of functional elements in the human genome. This phase of the project includes an expansion of assays that measure diverse RNA populations, identify proteins that interact with RNA and DNA, probe regions of DNA hypersensitivity, and measure ...
Cricket A. Sloan +16 more
openaire +2 more sources
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2022 Generative modeling and representation learning are two key tasks in computer vision. However, these models are typically trained independently, which ignores the potential for each task to help the other, and leads to training and model maintenance ...
Tianhong Li +5 more
semanticscholar +1 more source
LXMERT: Learning Cross-Modality Encoder Representations from Transformers [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities.
Hao Hao Tan, Mohit Bansal
semanticscholar +1 more source
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches [PDF]
SSST@EMNLP, 2014 Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder.
Kyunghyun Cho +3 more
semanticscholar +1 more source
SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation [PDF]
Computer Vision and Pattern Recognition, 2023 Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging.
Yuxuan Zhang +10 more
semanticscholar +1 more source
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation [PDF]
Computer Vision and Pattern Recognition, 2023 Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models, in which the key is to adapt the image-level ...
Bin Xie +4 more
semanticscholar +1 more source
On Pseudorandom Encodings [PDF]
, 2020 We initiate a study of pseudorandom encodings: efficiently computable and decodable encoding functions that map messages from a given distribution to a random-looking distribution. For instance, every distribution that can be perfectly and efficiently compressed admits such a pseudorandom encoding.
Agrikola, Thomas +4 more
openaire +5 more sources
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation [PDF]
Transactions of the Association for Computational Linguistics, 2021 Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly used models still require an explicit tokenization step.
J. Clark +3 more
semanticscholar +1 more source