Papers with computer vision tasks
Do we need Label Regularization to Fine-tune Pre-trained Language Models? (2023.eacl-main)
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Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh, Tianda Li, Alan Do-Omri, Peng Lu, Pascal Poupart, Ali Ghodsi
| Challenge: | Knowledge Distillation (KD) is a label regularization technique that can be replaced with lighter teacher-free variants such as the label-smoothing technique. |
| Approach: | They propose to use knowledge distillation to train student models by deploying the teacher network during training. |
| Outcome: | The proposed method can be replaced with lighter teacher-free variants on PLMs with more than 600 distinct trials and ran each configuration five times. |
CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Experimental results show that CLIP can be applied to zero-shot text classification tasks. |
| Approach: | They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP. |
| Outcome: | The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks. |
Automatically Learning Data Augmentation Policies for Dialogue Tasks (D19-1)
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| Challenge: | Recent advances in automatic data augmentation have focused on computer vision tasks where it is easy to apply imperceptible perturbations without changing an image’s semantic meaning. |
| Approach: | They adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. |
| Outcome: | The proposed algorithm reduces data-level model bias by using a controller trained on the target task. |
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets (2021.acl-long)
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| Challenge: | Large-scale pre-trained language models require enormous computational resources and long training time. |
| Approach: | They propose an algorithm to reduce inference time and train large NLP models by slimming the self-attention and fully-connected sub-layers inside a transformer. |
| Outcome: | The proposed algorithm achieves comparable performance to standard BERT with 35 45% less training time. |
Visual Named Entity Linking: A New Dataset and A Baseline (2022.findings-emnlp)
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| Challenge: | Existing tasks in Visual Entity Linking (VEL) rely on textual data to complement multi-modal linking or only link objects with general entities. |
| Approach: | They propose a task to link regions of images with corresponding entities in Knowledge Bases . they propose three sub-tasks, based on a human-annotated visual person dataset . |
| Outcome: | The proposed task is based on a human-annotated visual person linking dataset . the proposed sub-tasks are validated on the WIKIPerson dataset based upon the proposed methods . |
Evaluation of Deep Gaussian Processes for Text Classification (2020.lrec-1)
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| Challenge: | Existing models for text classification are limited by the expressability limit and require enormous empirical efforts to come up with a robust model architecture. |
| Approach: | They propose a Bayesian non-parametric Bayessian nonparametric model with strong function compositionality for the task of Text Classification. |
| Outcome: | The proposed models outperform shallow and deep Gaussian processes on the TREC (Text REtrieval Conference) datasets. |
A Self-Distillation Recipe for Neural Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods for Neural Machine Translation (NMT) have been proven effective in improving the performance of computer vision tasks without pre-training a teacher. |
| Approach: | They propose a rank-order augmented Pearson correlation loss and an iterative distillation method to prevent the discrepancy of predictions between the student and a stronger teacher from disturbing the training. |
| Outcome: | The proposed method can lead to significant improvements over the strong Transformer baseline on low/middle/high-resource tasks, obtaining comparable or better performance with fewer layers. |
SSMix: Saliency-Based Span Mixup for Text Classification (2021.findings-acl)
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| Challenge: | SSMix synthesizes a sentence while preserving the locality of two original texts by span-based mixing and keeping more tokens related to the prediction relying on saliency information. |
| Approach: | They propose a new method where the operation is performed on input text rather than on hidden vectors like previous approaches. |
| Outcome: | The proposed method outperforms hidden-level mixup methods on a wide range of text classification benchmarks including textual entailment, sentiment classification, and questiontype classification. |
Semi-Supervised Domain Adaptation for Emotion-Related Tasks (2023.findings-acl)
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| Challenge: | Semi-supervised domain adaptation (SSDA) is a model trained from a label-rich source domain to a new but related domain with a few labels of target data. |
| Approach: | They propose to decompose the semi-supervised domain adaptation framework into two subcomponents of unsupervised domain adaption (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target. |
| Outcome: | The proposed method is based on the co-learning of multiple classifiers for computer vision tasks and is published in the journal Nature. |
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization (2022.findings-emnlp)
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| Challenge: | Existing methods for knowledge distillation (KD) do not mitigate the noise in the teacher’s output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. |
| Approach: | They propose a method that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. |
| Outcome: | The proposed method achieves state-of-the-art performance on NLU and computer vision tasks. |
EBERT: Efficient BERT Inference with Dynamic Structured Pruning (2021.findings-acl)
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| Challenge: | Pruning has been demonstrated as an effective way of reducing computational complexity for deep networks, especially CNNs for computer vision tasks. |
| Approach: | They propose a dynamic structured pruning algorithm that prunes model weights at run-time . they propose to prune the unimportant heads in multi-head self-attention layers . |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on different tasks. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions (2020.acl-main)
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| Challenge: | Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero- shot image classification benchmarks. |
| Approach: | They propose a new paradigm for synthesizing task-specific neural networks from language descriptions and a generic pre-trained model from natural language. |
| Outcome: | The proposed model outperforms natural-language based zero-shot learning methods across 4 zero- shot image classification benchmarks. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
Robust Text Classification: Analyzing Prototype-Based Networks (2024.findings-emnlp)
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| Challenge: | Language models exhibit a drop in performance on noisy data, which can cause classifiers to incorrectly change their predictions. |
| Approach: | They propose to use Prototype-Based Networks to classify examples based on their similarity to prototypical examples of a class (prototypes) they show that PBNs offer more robustness under both targeted and static adversarial attacks. |
| Outcome: | The proposed model is robust to noise and targets both targeted and static attacks. |