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Found 12 papers, 10 papers with code
DistiLLM: Towards Streamlined Distillation for Large Language Models
Knowledge distillation (KD) is widely used for compressing a teacher model to a smaller student model, reducing its inference cost and memory footprint while preserving model capabilities.
Improving Adaptability and Generalizability of Efficient Transfer Learning for Vision-Language Models
Vision-Language Models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification.
Fine tuning Pre trained Models for Robustness Under Noisy Labels
To tackle this issue, researchers have explored methods for Learning with Noisy Labels to identify clean samples and reduce the influence of noisy labels.
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers.
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token.
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples.
Ranked #13 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs).
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks
Translation has played a crucial role in improving the performance on multilingual tasks: (1) to generate the target language data from the source language data for training and (2) to generate the source language data from the target language data for inference.
A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise
While existing methods address this problem in various directions, they still produce unpredictable sub-optimal results since they rely on the posterior information estimated by the feature extractor corrupted by noisy labels.
Self-Contrastive Learning
This paper proposes a novel contrastive learning framework, called Self-Contrastive (SelfCon) Learning, that self-contrasts within multiple outputs from the different levels of a multi-exit network.
Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network.
FINE Samples for Learning with Noisy Labels
Our framework, coined as filtering noisy instances via their eigenvectors (FINE), provides a robust detector with derivative-free simple methods having theoretical guarantees.
Ranked #2 on
Image Classification
on WebVision
