Search Results for author:
Found 10 papers, 10 papers with code
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Hence, many of the optimizations and tricks that have been successful in natural language generation may not be effective for code tasks.
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network
While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large datasets.
Ranked #2 on
Environmental Sound Classification
on UrbanSound8K
(using extra training data)
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results
The scheme, named USI (Unified Scheme for ImageNet), is based on knowledge distillation and modern tricks.
ML-Decoder: Scalable and Versatile Classification Head
In this paper, we introduce ML-Decoder, a new attention-based classification head.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations.
Ranked #1 on
Multi-Label Classification
on OpenImages-v6
ImageNet-21K Pretraining for the Masses
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks.
Ranked #2 on
Image Classification
on Stanford Cars
Asymmetric Loss For Multi-Label Classification
In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
Ranked #4 on
Multi-Label Classification
on NUS-WIDE
TResNet: High Performance GPU-Dedicated Architecture
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #7 on
Fine-Grained Image Classification
on Oxford 102 Flowers
(using extra training data)
XNAS: Neural Architecture Search with Expert Advice
This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.
ASAP: Architecture Search, Anneal and Prune
In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.
