Search Results for author:
Found 17 papers, 6 papers with code
DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow
Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion models by distilling via the diffusion model training objective.
Improving Diffusion Models for Virtual Try-on
Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.
Ranked #1 on
Virtual Try-on
on VITON-HD
Direct Consistency Optimization for Compositional Text-to-Image Personalization
In particular, our method results in a superior Pareto frontier to the baselines.
Collaborative Score Distillation for Consistent Visual Synthesis
Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities.
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures, and comics.
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation costs or have difficulties in collecting new samples for novel tasks.
Discovering and Mitigating Visual Biases through Keyword Explanation
The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names.
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization
In sum, we refer to our method as Guided Causal Invariant Syn-to-real Generalization that effectively improves the performance of syn-to-real generalization.
RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence
Here, the choice of data augmentation is sensitive to the quality of learned representations: as harder the data augmentations are applied, the views share more task-relevant information, but also task-irrelevant one that can hinder the generalization capability of representation.
Prototypical Contrastive Predictive Coding
Transferring representational knowledge of a model to another is a wide-ranging topic in machine learning.
Efficient randomized smoothing by denoising with learned score function
In this work, we present an efficient method for randomized smoothing that does not require any re-training of classifiers.
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR
Recurrent Neural Network Language Models (RNNLMs) have started to be used in various fields of speech recognition due to their outstanding performance.
Sequential Routing Framework: Fully Capsule Network-based Speech Recognition
Input sequences are capsulized then sliced by a window size.
Attention based on-device streaming speech recognition with large speech corpus
In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
end-to-end training of a large vocabulary end-to-end speech recognition system
Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).
Local Spectroscopies Reveal Percolative Metal in Disordered Mott Insulators
We elucidate the mechanism by which a Mott insulator transforms into a non-Fermi liquid metal upon increasing disorder at half filling.
Strongly Correlated Electrons Disordered Systems and Neural Networks
Accelerating recurrent neural network language model based online speech recognition system
This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems.
