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Found 50 papers, 24 papers with code
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Before deploying machine learning models it is critical to assess their robustness.
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB).
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Ranked #1 on
Image Classification
on CIFAR-10
Scalable Transfer Learning with Expert Models
We explore the use of expert representations for transfer with a simple, yet effective, strategy.
Ranked #11 on
Image Classification
on VTAB-1k
(using extra training data)
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision.
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts.
What Do Neural Networks Learn When Trained With Random Labels?
We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Predicting Neural Network Accuracy from Weights
Furthermore, the predictors are able to rank networks trained on different, unobserved datasets and with different architectures.
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open problem.
Big Transfer (BiT): General Visual Representation Learning
We conduct detailed analysis of the main components that lead to high transfer performance.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
(using extra training data)
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).
Ranked #15 on
Image Classification
on VTAB-1k
(using extra training data)
Semantic Bottleneck Scene Generation
For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.
Ranked #1 on
Image Generation
on Cityscapes-5K 256x512
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #10 on
Image Classification
on VTAB-1k
(using extra training data)
The Visual Task Adaptation Benchmark
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data.
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
MULEX: Disentangling Exploitation from Exploration in Deep RL
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour.
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation.
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled and unlabeled data.
Precision-Recall Curves Using Information Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace.
A Case for Object Compositionality in Deep Generative Models of Images
In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
FVD: A new Metric for Video Generation
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine learning.
Ranked #10 on
Conditional Image Generation
on ImageNet 128x128
Conditional Image Generation
Vocal Bursts Intensity Prediction
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
The Softmax function on top of a final linear layer is the de facto method to output probability distributions in neural networks.
Parameter-Efficient Transfer Learning for NLP
On GLUE, we attain within 0. 4% of the performance of full fine-tuning, adding only 3. 6% parameters per task.
Ranked #4 on
Image Classification
on OmniBenchmark
(using extra training data)
Towards Accurate Generative Models of Video: A New Metric & Challenges
To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
Investigating Object Compositionality in Generative Adversarial Networks
We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects.
Episodic Curiosity through Reachability
One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning.
On Self Modulation for Generative Adversarial Networks
Training Generative Adversarial Networks (GANs) is notoriously challenging.
A Large-Scale Study on Regularization and Normalization in GANs
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation.
On Accurate Evaluation of GANs for Language Generation
Generative Adversarial Networks (GANs) are a promising approach to language generation.
Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.
Competitive Training of Mixtures of Independent Deep Generative Models
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure.
Gradient Descent Quantizes ReLU Network Features
Deep neural networks are often trained in the over-parametrized regime (i. e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem.
Online Hyper-Parameter Optimization
We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters.
Are GANs Created Equal? A Large-Scale Study
Generative adversarial networks (GAN) are a powerful subclass of generative models.
Wasserstein Auto-Encoders
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution.
Critical Hyper-Parameters: No Random, No Cry
The selection of hyper-parameters is critical in Deep Learning.
Toward Optimal Run Racing: Application to Deep Learning Calibration
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand.
Better Text Understanding Through Image-To-Text Transfer
Generic text embeddings are successfully used in a variety of tasks.
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.
AdaGAN: Boosting Generative Models
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images.
