Search Results for author:
Found 43 papers, 19 papers with code
Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification
Previous studies have demonstrated that not each sample in a dataset is of equal importance during training.
SeCoKD: Aligning Large Language Models for In-Context Learning with Fewer Shots
Previous studies have shown that demonstrations can significantly help Large Language Models (LLMs ) perform better on the given tasks.
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection
In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems.
Generalized Categories Discovery for Long-tailed Recognition
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set comprising recognized classes.
ImbaGCD: Imbalanced Generalized Category Discovery
Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones.
Scaled Prompt-Tuning for Few-Shot Natural Language Generation
The increasingly Large Language Models (LLMs) demonstrate stronger language understanding and generation capabilities, while the memory demand and computation cost of fine-tuning LLMs on downstream tasks are non-negligible.
Supervised Knowledge May Hurt Novel Class Discovery Performance
Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance.
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
In this work, we propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents.
Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket
In this work, we revisit the potential of binary neural networks and focus on a compelling but unanswered problem: how can a binary neural network achieve the crucial accuracy level (e. g., 80%) on ILSVRC-2012 ImageNet?
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks
We further explore the limit of quantization bit and show that OCS could quantize BERT-Base and BERT-Large to 3-bits and retain 98% and 96% of the performance on the GLUE benchmark accordingly.
A Closer Look at Novel Class Discovery from the Labeled Set
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes.
BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.
Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data
One of the most pressing problems in the automated analysis of historical documents is the availability of annotated training data.
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications.
Ranked #950 on
Image Classification
on ImageNet
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing
The analysis of the compression effects in generative adversarial networks (GANs) after training, i. e. without any fine-tuning, remains an unstudied, albeit important, topic with the increasing trend of their computation and memory requirements.
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation
Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed.
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks.
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.
KISS: Keeping It Simple for Scene Text Recognition
Most of these methods propose novel building blocks for neural networks.
Back to Simplicity: How to Train Accurate BNNs from Scratch?
Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e. g., ImageNet.
Training Competitive Binary Neural Networks from Scratch
In our work, we focus on increasing the performance of binary neural networks without such prior knowledge and a much simpler training strategy.
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
We design a new conditional GAN with two components: a generative model and a discriminative model to mitigate imbalanced data problem through selective weighted loss.
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement network to mitigate unbalanced data problem through ensemble learning.
Learning to Train a Binary Neural Network
In our work, we focus on increasing our understanding of the training process and making it accessible to everyone.
microGAN: Promoting Variety through Microbatch Discrimination
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators
We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch.
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task.
SEE: Towards Semi-SupervisedEnd-to-End Scene Text Recognition
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task.
Ranked #2 on
Optical Character Recognition (OCR)
on FSNS - Test
Traversal-Free Word Vector Evaluation in Analogy Space
In this paper, we propose an alternative evaluating metric for word analogy questions (A to B is as C to D) in word vector evaluation.
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
In this paper, we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training.
Brain Abnormality Detection by Deep Convolutional Neural Network
In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations.
Deep Learning for Medical Image Analysis
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph. D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm.
Language Identification Using Deep Convolutional Recurrent Neural Networks
Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
STN-OCR: A single Neural Network for Text Detection and Text Recognition
In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way.
Ranked #5 on
Scene Text Detection
on ICDAR 2013
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations.
Punctuation Prediction for Unsegmented Transcript Based on Word Vector
In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript.
Image Captioning with Deep Bidirectional LSTMs
This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning.
