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Semantic ... \n", + "1 FUTURE-AI: Guiding Principles and Consensus Re... \n", + "2 Enforcing Mutual Consistency of Hard Regions f... \n", + "3 Parameter Decoupling Strategy for Semi-supervi... \n", + "4 Background-Foreground Segmentation for Interio... \n", + "5 EdgeFlow: Achieving Practical Interactive Segm... \n", + "6 Efficient Hybrid Transformer: Learning Global-... \n", + "7 Towards to Robust and Generalized Medical Imag... \n", + "8 Semi-supervised Meta-learning with Disentangle... \n", + "9 Semi-supervised Contrastive Learning for Label... \n", + "\n", + " summaries \\\n", + "0 Stereo matching is one of the widely used tech... \n", + "1 The recent advancements in artificial intellig... \n", + "2 In this paper, we proposed a novel mutual cons... \n", + "3 Consistency training has proven to be an advan... \n", + "4 To ensure safety in automated driving, the cor... \n", + "5 High-quality training data play a key role in ... \n", + "6 Semantic segmentation of fine-resolution 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0Survey on Semantic Stereo Matching / Semantic ...Stereo matching is one of the widely used tech...['cs.CV', 'cs.LG']
1FUTURE-AI: Guiding Principles and Consensus Re...The recent advancements in artificial intellig...['cs.CV', 'cs.AI', 'cs.LG']
2Enforcing Mutual Consistency of Hard Regions f...In this paper, we proposed a novel mutual cons...['cs.CV', 'cs.AI']
3Parameter Decoupling Strategy for Semi-supervi...Consistency training has proven to be an advan...['cs.CV']
4Background-Foreground Segmentation for Interio...To ensure safety in automated driving, the cor...['cs.CV', 'cs.LG']
5EdgeFlow: Achieving Practical Interactive Segm...High-quality training data play a key role in ...['cs.CV', 'cs.HC']
6Efficient Hybrid Transformer: Learning Global-...Semantic segmentation of fine-resolution urban...['cs.CV']
7Towards to Robust and Generalized Medical Imag...To mitigate the radiologist's workload, comput...['cs.CV', 'cs.AI']
8Semi-supervised Meta-learning with Disentangle...Generalising deep models to new data from new ...['cs.CV']
9Semi-supervised Contrastive Learning for Label...The success of deep learning methods in medica...['cs.CV']
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Although major advances\\nhave been made in the field, one recurring problem which remains unsolved is\\nthat of Catastrophic Forgetting (CF). While the issue has been extensively\\nstudied empirically, little attention has been paid from a theoretical angle.\\nIn this paper, we show that the impact of CF increases as two tasks\\nincreasingly align. We introduce a measure of task similarity called the NTK\\noverlap matrix which is at the core of CF. We analyze common projected gradient\\nalgorithms and demonstrate how they mitigate forgetting. Then, we propose a\\nvariant of Orthogonal Gradient Descent (OGD) which leverages structure of the\\ndata through Principal Component Analysis (PCA). Experiments support our\\ntheoretical findings and show how our method can help reduce CF on classical CL\\ndatasets.\",\n \"Few-shot learning is a challenging task since only few instances are given\\nfor recognizing an unseen class. One way to alleviate this problem is to\\nacquire a strong inductive bias via meta-learning on similar tasks. In this\\npaper, we show that such inductive bias can be learned from a flat collection\\nof unlabeled images, and instantiated as transferable representations among\\nseen and unseen classes. Specifically, we propose a novel part-based\\nself-supervised representation learning scheme to learn transferable\\nrepresentations by maximizing the similarity of an image to its discriminative\\npart. To mitigate the overfitting in few-shot classification caused by data\\nscarcity, we further propose a part augmentation strategy by retrieving extra\\nimages from a base dataset. We conduct systematic studies on miniImageNet and\\ntieredImageNet benchmarks. Remarkably, our method yields impressive results,\\noutperforming the previous best unsupervised methods by 7.74% and 9.24% under\\n5-way 1-shot and 5-way 5-shot settings, which are comparable with\\nstate-of-the-art supervised methods.\",\n \"Surgical instrument segmentation is extremely important for computer-assisted\\nsurgery. Different from common object segmentation, it is more challenging due\\nto the large illumination and scale variation caused by the special surgical\\nscenes. In this paper, we propose a novel bilinear attention network with\\nadaptive receptive field to solve these two challenges. For the illumination\\nvariation, the bilinear attention module can capture second-order statistics to\\nencode global contexts and semantic dependencies between local pixels. With\\nthem, semantic features in challenging areas can be inferred from their\\nneighbors and the distinction of various semantics can be boosted. For the\\nscale variation, our adaptive receptive field module aggregates multi-scale\\nfeatures and automatically fuses them with different weights. Specifically, it\\nencodes the semantic relationship between channels to emphasize feature maps\\nwith appropriate scales, changing the receptive field of subsequent\\nconvolutions. The proposed network achieves the best performance 97.47% mean\\nIOU on Cata7 and comes first place on EndoVis 2017 by 10.10% IOU overtaking\\nsecond-ranking method.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"terms\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3157,\n \"samples\": [\n \"['cs.LG', 'cs.CE', 'q-fin.ST', 'stat.ML']\",\n \"['cs.LG', 'physics.comp-ph', 'physics.flu-dyn']\",\n \"['cs.LG', 'cs.CV', 'math.AT']\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "titles=data[\"titles\"]" + ], + "metadata": { + "id": "9tY1-s5DKhLN" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "titles.head(5)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EZFXQhrLKhIL", + "outputId": 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"3fc2e7fdf5ab4c35930b5ca9f6d17829", + "23c542ef19e14c2c96800b06e198d4d4", + "66b04ca39cdd4ce88f57dd151b03bd02", + "120472b3c980426cbb6a94de946b22cf", + "b199ab56531b41f89865f3c6ff4eaa46", + "8fae0a8e187742cd92f24d13a4c0bda6", + "bac4cecad78a42b4a02f412de00b9deb", + "0c42d8eb4109469d89e1d515c2763b56", + "5c67c4b738be4c58b8a014f8c819c1e1", + "6b7154b5990442c0a8f932acffd848b2", + "4ccd9291ec214ea987ab36407117d496" + ] + }, + "id": "bE4mgdiEK5TA", + "outputId": "da1804cd-0a5f-47ea-dcfa-54b83e6c595b" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n", + "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.11.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from 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"69a836a252034d71bff7c45acdc2f447", + "d28c6aa96d2747218ceece76902e1045", + "4a1f610b42b946fb9d19de3c9ae4f229", + "12a99eb8a9d9446cae5ad510c47ae834", + "72828529515a4276ad01073282783851", + "c8c91d3521e1417f9e16311eeffe23a3", + "6fb2adf3541e4e2b897d4b78f8136f56", + "d716288a8de04d059161ea04fb647a2a", + "ae80249cec374106887176555c7407d1", + "f35acc7a8ce944a08f7a9986ba9c82cc", + "f4490f4836ce459b8db0071805d8f82e", + "66ccdcefbfb94ffc9b3e5cd14e33075f", + "fc1d252896204da2960e000b7b939835", + "3ecb13e113bf4c2fbf2eba8e786d7a8c", + "b6e5feeb9a37403f901d73549667e7ee", + "e03b826dc07d49098ba1317942449494", + "fbe18488c5c94d14a187439acd8458a7", + "8aebb30f242848f49627caca25f36069", + "d896f6781d6f484eb1179beee3a193fe", + "bba6fec2fc2f4449a8461e6726fdd3d6", + "b0ce5c604c404f01b867e4f0d1178846", + "42bebaa6fbfa4793bf5a8bae240e3055", + "9ea0f1d382c740658fc89da647b5dae6", + "96f76bbb9df0497bb0710de3fc6d3cec", + "65cab624c6df46b5be5a1375f0f21a54", + "29ef74622908424997540136de1e1be1", + "ddbea3e6c2154a6aa3fc4e44eca47221", + "5575bc7b7b604f1d83e03fe699dc3707" + ] + }, + "id": "GD4L6HrioTf-", + "outputId": "b2edee83-23b6-42e8-86dd-58120718700f" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
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"6185f519ac85414399201423cc37bf6a", + "d08f17ac25f4450c9a0e48b63ef2fd1a", + "4ae180c6f0824076936ac5d0efd81e07" + ] + }, + "id": "2I92Orhfkn5Q", + "outputId": "97322ed0-9861-48f4-b2e6-97ff14771579" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/90.9M [00:00