Search Results for author:
Found 27 papers, 19 papers with code
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
Ranked #1 on
Temporal/Casual QA
on NExT-QA
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
From this perspective, we hypothesise that instabilities in training GANs arise from the integration error in discretising the continuous dynamics.
End-to-End Adversarial Text-to-Speech
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest.
LOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate adversarial dynamics.
High Fidelity Speech Synthesis with Adversarial Networks
However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.
Adversarial Video Generation on Complex Datasets
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale.
Ranked #1 on
Video Generation
on Kinetics-600 48 frames, 64x64
3D Character Animation From A Single Photo
Video Generation
+1
Large Scale Adversarial Representation Learning
We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation.
Ranked #10 on
Contrastive Learning
on imagenet-1k
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.
Ranked #3 on
Conditional Image Generation
on ArtBench-10 (32x32)
Population Based Training of Neural Networks
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.
Visual Discovery at Pinterest
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
Adversarial Feature Learning
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution.
Context Encoders: Feature Learning by Inpainting
In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s).
Generating Visual Explanations
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself.
Data-dependent Initializations of Convolutional Neural Networks
Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable.
Visual Search at Pinterest
We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools.
Sequence to Sequence -- Video to Text
Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip.
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Solving the visual symbol grounding problem has long been a goal of artificial intelligence.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.
Ranked #3 on
Human Interaction Recognition
on BIT
LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories.
Part-based R-CNNs for Fine-grained Category Detection
Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts.
Ranked #62 on
Fine-Grained Image Classification
on CUB-200-2011
Caffe: Convolutional Architecture for Fast Feature Embedding
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
One-Shot Adaptation of Supervised Deep Convolutional Models
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
Rich feature hierarchies for accurate object detection and semantic segmentation
We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset.
Ranked #26 on
Object Detection
on PASCAL VOC 2007
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Images seen during test time are often not from the same distribution as images used for learning.
Semi-supervised Domain Adaptation with Instance Constraints
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets.
Efficient Learning of Domain-invariant Image Representations
We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.
