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Found 35 papers, 16 papers with code
Knowledge Graph Reasoning with Self-supervised Reinforcement Learning
To alleviate the distributional mismatch issue in general self-supervised RL (SSRL), in our supervised learning (SL) stage, the agent selects actions based on the policy network and learns from generated labels; this self-generation of labels is the intuition behind the name self-supervised.
Ar-Spider: Text-to-SQL in Arabic
The baselines demonstrate decent single-language performance on our Arabic text-to-SQL dataset, Ar-Spider, achieving 62. 48% for S2SQL and 65. 57% for LGESQL, only 8. 79% below the highest results achieved by the baselines when trained in English dataset.
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
As a result of the success of recent pre-trained models trained from larger-scale datasets, it is challenging to adapt the model to the DML tasks in the local data domain while retaining the previously gained knowledge.
Ranked #2 on
Image Retrieval
on iNaturalist
Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation
Our experiments on benchmarks, including the popular CUB-200-2011, CARS196, Stanford Online Products, and In-Shop Clothes Retrieval, show that our learning algorithm significantly improves the existing proxy losses and achieves superior results compared to the existing methods.
Multi-modal Domain Adaptation for REG via Relation Transfer
Domain adaptation, which aims to transfer knowledge between domains, has been well studied in many areas such as image classification and object detection.
On Calibrating Semantic Segmentation Models: Analyses and An Algorithm
Then, we study popular existing calibration methods and compare them with selective scaling on semantic segmentation calibration.
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.
Rapid Assessments of Light-Duty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning
Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions.
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks
We study protecting a user's data (images in this work) against a learner's unauthorized use in training neural networks.
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19
Next, we use simulated observation sequences to query the simulation system to retrieve simulated projection sequences as knowledge.
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning
Gradient-based meta-learning relates task-specific models to a meta-model by gradients.
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.
Ranked #6 on
Transferability
on classification benchmark
Cross-Domain Learning for Classifying Propaganda in Online Contents
Prior work has focused on supervised learning with training data from the same domain.
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words.
Improving Object Detection with Selective Self-supervised Self-training
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
Trace-Norm Adversarial Examples
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a $l_p$ neighborhood of the original image, the so-called distortion set.
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model
The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets.
BachGAN: High-Resolution Image Synthesis from Salient Object Layout
We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes.
Ranked #29 on
Long-tail Learning
on Places-LT
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
Attacking Lifelong Learning Models with Gradient Reversion
We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack.
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
In other words, there is a population of adversarial examples, instead of only one, for any input to a DNN.
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.
Learning to Adaptively Scale Recurrent Neural Networks
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series.
Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems
To the best of our knowledge, we are the first to provide stochastic and deterministic accelerated extension of APCD algorithms for general nonconvex and nonsmooth problems ensuring that for both bounded delays and unbounded delays every limit point is a critical point.
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
A model aware of the relationships between different domains can also be trained to work on new domains with less resources.
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios.
Multi-Stream Dynamic Video Summarization
With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content.
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity.
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train.
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability.
